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Evolving Workflow Orchestration // Alex Milowski // #291

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Manage episode 466662914 series 3241972
محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Alex Milowski is a researcher, developer, entrepreneur, mathematician, and computer scientist.

Evolving Workflow Orchestration // MLOps Podcast #291 with Alex Milowski, Entrepreneur and Computer Scientist.

// Abstract

There seems to be a shift from workflow languages to code, mostly annotation Python - happening and getting us. It is a symptom of how complex workflow orchestration has gotten. Is it a dominant trend or will we cycle back to “DAG specifications”? At Stitchfix, we had our own DSL that “compiled” into airflow DAGs, and at MicroByre, we used an external workflow language. Both had a batch task executor on K8s, but at MicroByre, we had human and robot in the loop workflows.

// Bio

Dr. Milowski is a serial entrepreneur and computer scientist with experience in a variety of data and machine learning technologies. He holds a PhD in Informatics (Computer Science) from the University of Edinburgh, where he researched large-scale computation over scientific data. Over the years, he's spent many years working on various aspects of workflow orchestration in industry, standardization, and research.

// Related Links

Website: https://www.milowski.com/

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

// MLOps Swag/Merch

⁠https://shop.mlops.community/⁠

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Alex on LinkedIn: https://www.linkedin.com/in/alexmilowski/

…   continue reading

473 قسمت

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Evolving Workflow Orchestration // Alex Milowski // #291

MLOps.community

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پخش پخش کردن
iconاشتراک گذاری
 
MP3•خانه قسمت
Manage episode 466662914 series 3241972
محتوای ارائه شده توسط Demetrios. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Demetrios یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Alex Milowski is a researcher, developer, entrepreneur, mathematician, and computer scientist.

Evolving Workflow Orchestration // MLOps Podcast #291 with Alex Milowski, Entrepreneur and Computer Scientist.

// Abstract

There seems to be a shift from workflow languages to code, mostly annotation Python - happening and getting us. It is a symptom of how complex workflow orchestration has gotten. Is it a dominant trend or will we cycle back to “DAG specifications”? At Stitchfix, we had our own DSL that “compiled” into airflow DAGs, and at MicroByre, we used an external workflow language. Both had a batch task executor on K8s, but at MicroByre, we had human and robot in the loop workflows.

// Bio

Dr. Milowski is a serial entrepreneur and computer scientist with experience in a variety of data and machine learning technologies. He holds a PhD in Informatics (Computer Science) from the University of Edinburgh, where he researched large-scale computation over scientific data. Over the years, he's spent many years working on various aspects of workflow orchestration in industry, standardization, and research.

// Related Links

Website: https://www.milowski.com/

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch all episodes, blogs, newsletters, and more: https://mlops.community/

// MLOps Swag/Merch

⁠https://shop.mlops.community/⁠

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Alex on LinkedIn: https://www.linkedin.com/in/alexmilowski/

…   continue reading

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The Unbearable Lightness of Data // MLOps Podcast #295 with Rohit Krishnan, Chief Product Officer at bodo.ai. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Rohit Krishnan, Chief Product Officer at Bodo.AI, joins Demetrios to discuss AI's evolving landscape. They explore interactive reasoning models, AI's impact on jobs, scalability challenges, and the path to AGI. Rohit also shares insights on Bodo.AI’s open-source move and its impact on data science. // Bio Building products, writing, messing around with AI pretty much everywhere // Related Links Website: www.strangeloopcanon.com In life, my kids. Professionally, https://github.com/bodo-ai/Bodo ... Otherwise, personally, it's writing every single day at strangeloopcanon.com ! ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or [LinkedIn]( https://go.mlops.community/linkedin )] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Rohit on LinkedIn: /rkris Timestamps:[00:00] Rohit's preferred coffee[00:12] Takeaways[00:46] DeepSeek UI Insight[07:07] Signal vs. Noise Feedback[09:24] Report refinement process[11:54] Cognitive load and model control[15:27] Future AI possibilities[18:47] AI data labeling jobs[21:27] AI noise and overhead[26:12] Information Overload & Attention[29:52] AGI Progress Vectors Explained[37:28] GPU Memory Bottlenecks in AI[46:23] AI vs ML Engineering[50:22] Parsing PDFs with Metadata[53:24] Wrap up…
 
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Kubernetes, AI Gateways, and the Future of MLOps // MLOps Podcast #294 with Alexa Griffith, Senior Software Engineer at Bloomberg. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Alexa shares her journey into software engineering, from early struggles with Airflow and Kubernetes to leading open-source projects like the Envoy AI Gateway. She and Demetrios discuss AI model deployment, tooling differences across tech roles, and the importance of abstraction. They highlight aligning technical work with business goals and improving cross-team communication, offering key insights into MLOps and AI infrastructure. // Bio Alexa Griffith is a Senior Software Engineer at Bloomberg, where she builds scalable inference platforms for machine learning workflows and contributes to open-source projects like KServe. She began her career at Bluecore working in data science infrastructure, and holds an honors degree in Chemistry from the University of Tennessee, Knoxville. She shares her insights through her podcast, Alexa’s Input (AI), technical blogs, and active engagement with the tech community at conferences and meetups. // Related Links Website: https://alexagriffith.com/ Kubecon Keynote about Envoy AI Gateway https://www.youtube.com/watch?v=do1viOk8nok ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ][ https://x.com/mlopscommunity ] or LinkedIn [ https://go.mlops.community/linkedin ] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Alexa on LinkedIn: /alexa-griffith Timestamps:[00:00] Alexa's preferred coffee[00:13] Takeaways [02:39] Airflow Kubernetes Pain Points[07:35] Pipelining Tools Landscape[12:11] KubeCon AI Gateway Keynote[14:21] Envoy vs Envoy AI Gateway[20:53] AI Model Routing Flexibility[25:15] Celery and Airflow Integration[26:25] Open Source Contribution Tips[32:03] ML Platform Adoption Tips[37:20] Simplicity Over Complexity[40:42] Engaging Non-Technical Stakeholders[44:19] ML vs LLM Platform Divergence[48:27] LLM vs ML Models[49:47] Wrap up…
 
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Future of Software, Agents in the Enterprise, and Inception Stage Company Building // MLOps Podcast 293 with Eliot Durbin, General Partner at Boldstart Ventures. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Key lessons for founders who are thinking about or starting their companies. 15 years of inception stage investing from how data science companies like Yhat went to market in 2013-14 and how that's evolved, to building companies around OSS frameworks like CrewAI; Eliot shares key learnings and questions for founders starting out. // Bio Eliot has been a General Partner @ Boldstart Ventures since its founding in 2010. Boldstart is an inception-stage lead investor for technical founders building the next generation of enterprise companies such as Clay, Snyk, BigID, Kustomer, Superhuman, and CrewAI. // Related Links Website: boldstart.vc https://medium.com/@etdurbin ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or LinkedIn ( https://go.mlops.community/linkedin ) Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Eliot on LinkedIn: /eliotdurbin Timestamps:[00:00] Eliot's preferred coffee[00:32] Takeaways[01:36] Investing Regret Lesson[03:20] Founder-Market Fit Exploration[06:05] Exciting AI Missions[16:15] AI Integration Dilemmas[21:28] Open-source Stack Concerns[25:30] AI Agent Interaction Layers[30:02] ChatOps Agent Orchestration[33:18] Agent Pricing Challenges[38:37] Evals in Agent Systems[45:43] Strong Opinions and Enablement[48:07] Gemini OCR Replacement[51:56] Wrap up…
 
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Agents in Production [Podcast Limited Series] - Episode Five, Dmitri Jarnikov, Chiara Caratelli, and Steven Vester join Demetrios to explore AI agents in e-commerce. They discuss the trade-offs between generic and specialized agents, with Dmitri noting the need for a balance between scalability and precision. Chiara highlights how agents can dynamically blend both approaches, while Steven predicts specialized agents will dominate initially before trust in generic agents grows. The panel also examines how e-commerce platforms may resist but eventually collaborate with AI agents. Trust remains a key factor in adoption, with opportunities emerging for new agent-driven business models. Guest speakers: Dmitri Jarnikov - Senior Director of Data Science at Prosus Chiara Caratelli - Data Scientist at Prosus Group Steven Vester - Head of Product at OLX Host: Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ]( https://x.com/mlopscommunity ) or [LinkedIn]( https://go.mlops.community/linkedin) ] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm…
 
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Talk to Your Data: The SQL Data Analyst 53:56

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In Agents in Production [Podcast Limited Series] - Episode Four , Donné Stevenson and Paul van der Boor break down the deployment of a Token Data Analyst agent at Prosus—why, how, and what worked. They discuss the challenges of productionizing the agent, from architecture to mitigating LLM overconfidence, key design choices, the role of pre-checks for clarity, and why they opted for simpler text-based processes over complex recursive methods. Guest speakers: Paul van der Boor - VP AI at Prosus Group Donne Stevenson - Machine Learning Engineer at Prosus Group Host: Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ][ https://x.com/mlopscommunity ] or LinkedIn [ https://go.mlops.community/linkedin ] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm…
 
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Getting to Grips with Web Agents 45:52

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Agents in Production [Podcast Limited Series] - Episode Three explores the concept of web agents—AI-powered systems that interact with the web as humans do, navigating browsers instead of relying solely on APIs. The discussion covers why web agents emerge as a natural step in AI evolution, their advantages over API-based systems, and their potential impact on e-commerce and automation. The conversation also highlights challenges in making websites agent-friendly and envisions a future where agents seamlessly handle tasks like booking flights or ordering food. Guest speakers: Paul van der Boor - VP AI at Prosus Group Chiara Caratelli - Data Scientist at Prosus Group Host: Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ][ https://x.com/mlopscommunity ] or LinkedIn [ https://go.mlops.community/linkedin ] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm…
 
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The Challenge with Voice Agents 47:37

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In Agents in Production Series - Episode Two , Demetrios, Paul, and Floris explore the latest in Voice AI agents. They discuss real-time voice interactions, OpenAI's real-time Voice API, and real-world deployment challenges. Paul shares insights from iFood’s voice AI tests in Brazil, while Floris highlights technical hurdles like turn detection and language processing. The episode covers broader applications in healthcare and customer service, emphasizing continuous learning and open-source innovation in Voice AI. Guest speakers: Paul van der Boor - VP AI at Prosus Group Floris Fok - AI Engineer at Prosus Group Host:Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our slack community [ https://go.mlops.community/slack ] Follow us on X/Twitter [ @mlopscommunity ][ https://x.com/mlopscommunity ] or LinkedIn [ https://go.mlops.community/linkedin ] Sign up for the next meetup: [ https://go.mlops.community/register ] MLOps Swag/Merch: [ https://shop.mlops.community/ ] Connect with Demetrios on LinkedIn: /dpbrinkm…
 
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The Agent Landscape - Lessons Learned Putting Agents Into Production 1:08:40

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In Agents in Production Series - Episode One , Demetrios chats with Paul van der Boor and Floris Fok about the real-world challenges of deploying AI agents across @ProsusGroup of companies. They break down the evolution from simple LLMs to fully interactive systems, tackling scale, UX, and the harsh lessons from failed projects. Packed with insights on what works (and what doesn’t), this episode is a must-listen for anyone serious about AI in production. Guest speakers: Paul van der Boor - VP AI at Prosus Group Floris Fok - AI Engineer at Prosus Group Host: Demetrios Brinkmann - Founder of MLOps Community ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: ⁠https://go.mlops.community/TYExplore⁠ Join our Slack community [ ⁠https://go.mlops.community/slack⁠ ] Follow us on X/Twitter [ ⁠@mlopscommunity⁠ ][ ⁠https://x.com/mlopscommunity⁠ ] or LinkedIn [ ⁠https://go.mlops.community/linkedin⁠ ] Sign up for the next meetup: [ ⁠https://go.mlops.community/register⁠ ] MLOps Swag/Merch: [ ⁠https://shop.mlops.community/⁠ ] Connect with Demetrios on LinkedIn: ⁠/dpbrinkm…
 
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Evolving Workflow Orchestration // Alex Milowski // #291 1:14:34

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Alex Milowski is a researcher, developer, entrepreneur , mathematician, and computer scientist . Evolving Workflow Orchestration // MLOps Podcast #291 with Alex Milowski, Entrepreneur and Computer Scientist. // Abstract There seems to be a shift from workflow languages to code, mostly annotation Python - happening and getting us. It is a symptom of how complex workflow orchestration has gotten. Is it a dominant trend or will we cycle back to “DAG specifications”? At Stitchfix, we had our own DSL that “compiled” into airflow DAGs, and at MicroByre, we used an external workflow language. Both had a batch task executor on K8s, but at MicroByre, we had human and robot in the loop workflows. // Bio Dr. Milowski is a serial entrepreneur and computer scientist with experience in a variety of data and machine learning technologies. He holds a PhD in Informatics (Computer Science) from the University of Edinburgh, where he researched large-scale computation over scientific data. Over the years, he's spent many years working on various aspects of workflow orchestration in industry, standardization, and research. // Related Links Website: https://www.milowski.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ // MLOps Swag/Merch ⁠https://shop.mlops.community/⁠ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Alex on LinkedIn: https://www.linkedin.com/in/alexmilowski/…
 
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Insights from Cleric: Building an Autonomous AI SRE // Willem Pienaar // #290 55:57

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Willem Pienaar is the Co-Founder and CTO of Cleric . He previously worked at Tecton as a Principal Engineer. Willem Pienaar attended the Georgia Institute of Technology. Insights from Cleric: Building an Autonomous AI SRE // MLOps Podcast #289 with Willem Pienaar, CTO & Co-Founder of Cleric. // Abstract In this MLOps Community Podcast episode, Willem Pienaar, CTO of Cleric, breaks down how they built an autonomous AI SRE that helps engineering teams diagnose production issues. We explore how Cleric builds knowledge graphs for system understanding and uses existing tools/systems during investigations. We also get into some gnarly challenges around memory, tool integration, and evaluation frameworks, and some lessons learned from deploying to engineering teams. // Bio Willem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories.Before starting Cleric, Willem led the open-source engineering team at Tecton and established the ML platform team at Gojek, where he built high-scale ML systems for the Southeast Asian Decacorn. // Related Links Website: willem.co --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ // MLOps Swag/Merch ⁠https://shop.mlops.community/⁠ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Willem on LinkedIn: https://www.linkedin.com/in/willempienaar/…
 
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Robustness, Detectability, and Data Privacy in AI // Vinu Sankar Sadasivan // #289 52:59

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Vinu Sankar Sadasivan is a CS PhD ... Currently, I am working as a full-time Student Researcher at Google DeepMind on jailbreaking multimodal AI models.Robustness, Detectability, and Data Privacy in AI // MLOps Podcast #289 with Vinu Sankar Sadasivan, Student Researcher at Google DeepMind.// AbstractRecent rapid advancements in Artificial Intelligence (AI) have made it widely applicable across various domains, from autonomous systems to multimodal content generation. However, these models remain susceptible to significant security and safety vulnerabilities. Such weaknesses can enable attackers to jailbreak systems, allowing them to perform harmful tasks or leak sensitive information. As AI becomes increasingly integrated into critical applications like autonomous robotics and healthcare, the importance of ensuring AI safety is growing. Understanding the vulnerabilities in today’s AI systems is crucial to addressing these concerns.// BioVinu Sankar Sadasivan is a final-year Computer Science PhD candidate at The University of Maryland, College Park, advised by Prof. Soheil Feizi. His research focuses on Security and Privacy in AI, with a particular emphasis on AI robustness, detectability, and user privacy. Currently, Vinu is a full-time Student Researcher at Google DeepMind, working on jailbreaking multimodal AI models. Previously, Vinu was a Research Scientist intern at Meta FAIR in Paris, where he worked on AI watermarking.Vinu is a recipient of the 2023 Kulkarni Fellowship and has earned several distinctions, including the prestigious Director’s Silver Medal. He completed a Bachelor’s degree in Computer Science & Engineering at IIT Gandhinagar in 2020. Prior to their PhD, Vinu gained research experience as a Junior Research Fellow in the Data Science Lab at IIT Gandhinagar and through internships at Caltech, Microsoft Research India, and IISc.// MLOps Swag/Merch https://shop.mlops.community/ // Related LinksWebsite: https://vinusankars.github.io/ --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Richard on LinkedIn: https://www.linkedin.com/in/vinusankars/…
 
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AI & Aliens: New Eyes on Ancient Questions // Richard Cloete // #288 47:58

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Richard Cloete is a computer scientist and a Laukien-Oumuamua Postdoctoral Research Fellow at the Center for Astrophysics, Harvard University. He is a member of the Galileo Project working under the supervision of Professor Avi, having recently held a postdoctoral position at the University of Cambridge, UK.AI & Aliens: New Eyes on Ancient Questions // MLOps Podcast #288 with Richard Cloete, Laukien-Oumuamua Postdoctoral Research Fellow at Harvard University.// AbstractDemetrios speaks with Dr. Richard Cloete, a Harvard computer scientist and founder of SEAQR Robotics, about his AI-driven work in tracking Unidentified Aerial Phenomena (UAPs) through the Galileo Project. Dr. Cloete explains their advanced sensor setup and the challenges of training AI in this niche field, leading to the creation of AeroSynth, a synthetic data tool.He also discusses his collaboration with the Minor Planet Center on using AI to classify interstellar objects and upcoming telescope data. Additionally, he introduces Seeker Robotics, applying similar AI techniques to oceanic research with unmanned vehicles for marine monitoring. The conversation explores AI’s role in advancing our understanding of space and the ocean.// BioRichard is a computer scientist and Laukien-Oumuamua Postdoctoral Research Fellow at the Center for Astrophysics, Harvard University. As a member of the Galileo Project under Professor Avi Loeb's supervision, he develops AI models for detecting and tracking aerial objects, specializing in Unidentified Anomalous Phenomena (UAP). Beyond UAP research, he collaborates with astronomers at the Minor Planet Center to create AI models for identifying potential interstellar objects using the upcoming Vera C. Rubin Observatory. Richard is also the CEO and co-founder of SEAQR Robotics, a startup developing advanced unmanned surface vehicles to accelerate the discovery of novel life and phenomena in Earth's oceans and atmosphere. Before joining Harvard, he completed a postdoctoral fellowship at the University of Cambridge, UK, where his research explored the intersection of emerging technologies and law.Grew up in Cape Town, South Africa, where I used to build Tesla Coils, plasma globes, radio stethoscopes, microwave guns, AM radios, and bombs...// MLOps Swag/Merch https://shop.mlops.community/ // Related LinksWebsite: www.seaqr.net https://itc.cfa.harvard.edu/people/richard-cloete --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Richard on LinkedIn: https://www.linkedin.com/in/richard-cloete/…
 
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Real LLM Success Stories: How They Actually Work // Alex Strick van Linschoten // #287 49:54

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A software engineer based in Delft, Alex Strick van Linschoten recently built Ekko, an open-source framework for adding real-time infrastructure and in-transit message processing to web applications. With years of experience in Ruby, JavaScript, Go, PostgreSQL, AWS, and Docker, I bring a versatile skill set to the table. I hold a PhD in History, have authored books on Afghanistan, and currently work as an ML Engineer at ZenML .Real LLM Success Stories: How They Actually Work // MLOps Podcast #287 with Alex Strick van Linschoten, ML Engineer at ZenML.// AbstractAlex Strick van Linschoten, a machine learning engineer at ZenML, joins the MLOps Community podcast to discuss his comprehensive database of real-world LLM use cases. Drawing inspiration from Evidently AI, Alex created the database to organize fragmented information on LLM usage, covering everything from common chatbot implementations to innovative applications across sectors. They discuss the technical challenges and successes in deploying LLMs, emphasizing the importance of foundational MLOps practices. The episode concludes with a call for community contributions to further enrich the database and collective knowledge of LLM applications.// BioAlex is a Software Engineer based in the Netherlands, working as a Machine Learning Engineer at ZenML. He previously was awarded a PhD in History (specialism: War Studies) from King's College London and has authored several critically acclaimed books based on his research work in Afghanistan.// MLOps Swag/Merch https://shop.mlops.community/ // Related LinksWebsite: https://mlops.systems https://www.zenml.io/llmops-database https://www.zenml.io/llmops-database https://www.zenml.io/blog/llmops-in-production-457-case-studies-of-what-actually-works https://www.zenml.io/blog/llmops-lessons-learned-navigating-the-wild-west-of-production-llms https://www.zenml.io/blog/demystifying-llmops-a-practical-database-of-real-world-generative-ai-implementations https://huggingface.co/datasets/zenml/llmops-database --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Alex on LinkedIn: https://www.linkedin.com/in/strickvl Timestamps:[00:00] Alex's preferred tea[00:15] Takeaways[00:55] LLM Database Creation Insights[03:26] Hidden Gems and LLMs[07:04] Chatbot Governance Challenges[15:16] AI Agents and IPOs[19:51] AI Interface Evolution[23:56] LLMs as Product Guides[26:57] RAG with User Context[30:29] User Experience Friction Points[36:20] ROI and Engineering Insights[41:09] Agent Debugging and Flows[45:28] Data Viz Ideas LLM[47:41] Wrap up…
 
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Navigating Machine Learning Careers: Insights from Meta to Consulting // Ilya Reznik // #286 1:00:36

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In his 13 years of software engineering, Ilya Reznik has specialized in commercializing machine learning solutions and building robust ML platforms. He's held technical lead and staff engineering roles at premier firms like Adobe, Twitter, and Meta. Currently, Ilya channels his expertise into his travel startup, Jaunt, while consulting and advising emerging startups.Navigating Machine Learning Careers: Insights from Meta to Consulting // MLOps Podcast #286 with Ilya Reznik, ML Engineering Thought Leader at Instructed Machines, LLC.// AbstractIlya Reznik's insights into machine learning and career development within the field. With over 13 years of experience at leading tech companies such as Meta, Adobe, and Twitter, Ilya emphasizes the limitations of traditional model fine-tuning methods. He advocates for alternatives like prompt engineering and knowledge retrieval, highlighting their potential to enhance AI performance without the drawbacks associated with fine-tuning.Ilya's recent discussions at the NeurIPS conference reflect a shift towards practical applications of Transformer models and innovative strategies like curriculum learning. Additionally, he shares valuable perspectives on navigating career progression in tech, offering guidance for aspiring ML engineers aiming for senior roles. His narrative serves as a blend of technical expertise and practical career advice, making it a significant resource for professionals in the AI domain.// BioIlya has navigated a diverse career path since 2011, transitioning from physicist to software engineer, data scientist, ML engineer, and now content creator. He is passionate about helping ML engineers advance their careers and making AI more impactful and beneficial for society.Previously, Ilya was a technical lead at Meta, where he contributed to 12% of the company’s revenue and managed approximately 30 production ML models. He also worked at Twitter, overseeing offline model evaluation, and at Adobe, where his team was responsible for all intelligent services within Adobe Analytics.Based in Salt Lake City, Ilya enjoys the outdoors, tinkering with Arduino electronics, and, most importantly, spending time with his family.// MLOps Swag/Merch https://shop.mlops.community/ // Related LinksWebsite: mlepath.com --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ilya on LinkedIn: https://www.linkedin.com/in/ibreznik/ Timestamps:[00:00] Ilya's preferred coffee[00:12] Takeaways[01:10] Fine-tuning: Pros and Cons[02:26] Fine-tuning Gamble[07:50] LLM Architecture Tradeoffs[11:37] NeurIPS Takeaways and Insights[22:42] AI Benchmarking and Data Leaks[29:30] Staff ML Engineer Path[40:08] Staff Engineer Titles Explained[49:00] E6 to E7 Expectations[51:43] Tech Jobs vs Startups[55:33] Senior to Staff Journey[58:49] Wrap up…
 
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Collective Memory for AI on Decentralized Knowledge Graph // Tomaž Levak // #285 53:24

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Tomaž Levak is the Co-founder and CEO of Trace Labs – OriginTrail core developers. OriginTrail is a web3 infrastructure project combining a decentralized knowledge graph (DKG) and blockchain technologies to create a neutral, inclusive ecosystem.Collective Memory for AI on Decentralized Knowledge Graph // MLOps Podcast #285 with Tomaz Levak, Founder of Trace Labs, Core Developers of OriginTrail.// AbstractThe talk focuses on how OriginTrail Decentralized Knowledge Graph serves as a collective memory for AI and enables neuro-symbolic AI. We cover the basics of OriginTrail’s symbolic AI fundamentals (i.e., knowledge graphs) and go over the details of how decentralization improves data integrity, provenance, and user control. We’ll cover the DKG role in AI agentic frameworks and how it helps with verifying and accessing diverse data sources, while maintaining compatibility with existing standards. We’ll explore practical use cases from the enterprise sector as well as the latest integrations into frameworks like ElizaOS. We conclude by outlining the future potential of decentralized AI, AI becoming the interface to “eat” SaaS, and the general convergence of AI, Internet, and Crypto.// BioTomaz Levak, founder of OriginTrail, is active at the intersection of Cryptocurrency, the Internet, and Artificial Intelligence (AI). At the core of OriginTrail is a pursuit of Verifiable Internet for AI, an inclusive framework addressing critical challenges of the world in an AI era. To achieve the goal of Verifiable Internet for AI, OriginTrail's trusted knowledge foundation ensures the provenance and verifiability of information while incentivizing the creation of high-quality knowledge. These advancements are pivotal to unlock the full potential of AI as they minimize the technology’s shortfalls, such as hallucinations, bias, issues of data ownership, and model collapse.Tomaz's contributions to OriginTrail span over a decade and across multiple fields. He is involved in strategic technical innovations for OriginTrail Decentralized Knowledge Graph (DKG) and NeuroWeb blockchain and was among the authors of all three foundational White Paper documents that defined how OriginTrail technology addresses global challenges. Tomaz contributed to the design of OriginTrail token economies and is driving adoption with global brands such as British Standards Institution, Swiss Federal Railways, and World Federation of Haemophilia, among others.Committed to the ongoing expansion of the OriginTrail ecosystem, Tomaz is a regular speaker at key industry events. In his appearances, he highlights the significant value that the OriginTrail DKG brings to diverse sectors, including supply chains, life sciences, healthcare, and scientific research. In a rapidly evolving digital landscape, Tomaz and the OriginTrail ecosystem as a whole are playing an important role in ensuring a more inclusive, transparent, and decentralized AI.// MLOps Swag/Merch https://shop.mlops.community/ // Related LinksWebsite: https://origintrail.io Song recommendation: https://open.spotify.com/track/5GGHmGNZYnVSdRERLUSB4w?si=ae744c3ad528424b --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Tomaz on LinkedIn: https://www.linkedin.com/in/tomazlevak/ Timestamps:[00:00] Tomaz's preferred coffee[00:20] Takeaways[01:08] Knowledge Graphs and V8[02:47] Knowledge Graph Neighborhoods[07:08] DKG Enterprise Value Prop[17:33] DKG Multi-Chain Agent Use[23:40] DKG Use Cases[26:57] Wearables Data Integration Ideas[33:19] Knowledge Graph Value Models[36:14] Replacing Bad Information[44:13] Node Hosting Responsibilities[50:47] Wrap up…
 
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Efficient Deployment of Models at the Edge // Krishna Sridhar // #284 51:33

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Krishna Sridhar is an experienced engineering leader passionate about building wonderful products powered by machine learning.Efficient Deployment of Models at the Edge // MLOps Podcast #284 with Krishna Sridhar, Vice President of Qualcomm. Big shout-out to Qualcomm for sponsoring this episode!// AbstractQualcomm® AI Hub helps to optimize, validate, and deploy machine learning models on-device for vision, audio, and speech use cases.With Qualcomm® AI Hub, you can:Convert trained models from frameworks like PyTorch and ONNX for optimized on-device performance on Qualcomm® devices.Profile models on-device to obtain detailed metrics, including runtime, load time, and compute unit utilization.Verify numerical correctness by performing on-device inference.Easily deploy models using Qualcomm® AI Engine Direct, TensorFlow Lite, or ONNX Runtime.The Qualcomm® AI Hub Models repository contains a collection of example models that use Qualcomm® AI Hub to optimize, validate, and deploy models on Qualcomm® devices.Qualcomm® AI Hub automatically handles model translation from source framework to device runtime, applying hardware-aware optimizations, and performs physical performance/numerical validation. The system automatically provisions devices in the cloud for on-device profiling and inference. The following image shows the steps taken to analyze a model using Qualcomm® AI Hub.// BioKrishna Sridhar leads engineering for Qualcomm™ AI Hub, a system used by more than 10,000 AI developers spanning 1,000 companies to run more than 100,000 models on Qualcomm platforms. Prior to joining Qualcomm, he was Co-founder and CEO of Tetra AI, which made it easy to efficiently deploy ML models on mobile/edge hardware. Prior to Tetra AI, Krishna helped design Apple's CoreML, which was a software system mission-critical to running several experiences at Apple, including Camera, Photos, Siri, FaceTime, Watch, and many more across all major Apple device operating systems and all hardware and IP blocks. He has a Ph.D. in computer science from the University of Wisconsin-Madison and a bachelor’s degree in computer science from Birla Institute of Technology and Science, Pilani, India.// MLOps Swag/Merch https://shop.mlops.community/ // Related LinksWebsite: https://www.linkedin.com/in/srikris/ --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Krishna on LinkedIn: https://www.linkedin.com/in/srikris/ Timestamps:[00:00] Krishna's preferred coffee[00:12] Takeaways[01:27] Please like, share, leave a review, and subscribe to our MLOps channels![01:56] AI Entrepreneurship Journey[04:25] Core ML and Edge AI[08:44] AI Stack & Workflow Strategy[11:42] On-device AI Foundations[17:15] Hardware vs Software Optimization[21:32] On-device AI Challenges[26:19] Small LLM Orchestration[28:03] Memory Constraints and Shared Pools[30:05] Qualcomm AI Hub Edge[32:53] AI in Unexpected Places[41:53] Deploying AI on Edge[45:58] 4X Battery Optimization Tips[51:00] Wrap up…
 
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Real World AI Agent Stories // Zach Wallace // #283 47:07

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Machine Learning, AI Agents, and Autonomy // MLOps Podcast #283 with Zach Wallace, Staff Software Engineer at Nearpod Inc.// AbstractDemetrios chats with Zach Wallace, engineering manager at Nearpod, about integrating AI agents in e-commerce and edtech. They discuss using agents for personalized user targeting, adapting AI models with real-time data, and ensuring efficiency through clear task definitions. Zach shares how Nearpod streamlined data integration with tools like Redshift and DBT, enabling real-time updates. The conversation covers challenges like maintaining AI in production, handling high-quality data, and meeting regulatory standards. Zach also highlights the cost-efficiency framework for deploying and decommissioning agents and the transformative potential of LLMs in education.// BioSoftware Engineer with 10 years of experience. Started my career as an Application Engineer, but I have transformed into a Platform Engineer. As a Platform Engineer, I have handled the problems described below - Localization across 6-7 different languages - Building a custom local environment tool for our engineers - Building a Data Platform - Building standards and interfaces for Agentic AI within ed-tech.// MLOps Swag/Merch https://shop.mlops.community/ // Related Links https://medium.com/renaissance-learning-r-d/data-platform-transform-a-data-monolith-9d5290a552ef --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: /dpbrinkm/ Connect with Zach on LinkedIn: https: /zachary-wallace/ Timestamps:[00:00] Zach's preferred coffee[00:24] Takeaways[01:25] Data platform pivot[04:06] Data integration with DBT[06:50] Data mesh partial adoption[08:55] Data product[10:11] Agent Architectures and Deployment[15:35] Agent vs LLM[20:28] AI Agent Analytics[22:18] Agent Design and Scope[26:52] DAG Agent Workflow Design[30:25] Cost Considerations in AI[35:00] Agent Deployment and Costing[42:25] AI Evaluation Use Cases[45:25] Agent vs ML for Contracts[46:55] Wrap up…
 
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Machine Learning, AI Agents, and Autonomy // Egor Kraev // #282 1:05:20

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For three years, Egor has been bringing the power of AI to bear at Wise , across domains as varied as trading algorithms for Treasury, fraud detection, experiment analysis, causal inference, and, recently, the numerous applications unlocked by large language models. Open-source projects initiated and guided by Egor include wise-pizza, causaltune, and neural-lifetimes, with more on the way. Machine Learning, AI Agents, and Autonomy // MLOps Podcast #282 with Egor Kraev, Head of AI at Wise Plc. // Abstract Demetrios chats with Egor Kraev, principal AI scientist at Wise, about integrating large language models (LLMs) to enhance ML pipelines and humanize data interactions. Egor discusses his open-source MotleyCrew framework, career journey, and insights into AI's role in fintech, highlighting its potential to streamline operations and transform organizations. // Bio Egor first learned mathematics in the Russian tradition, then continued his studies at ETH Zurich and the University of Maryland. Egor has been doing data science since the last century, including economic and human development data analysis for nonprofits in the US, the UK, and Ghana, and 10 years as a quant, solutions architect, and occasional trader at UBS, then Deutsche Bank. Following last decade's explosion in AI techniques, Egor became Head of AI at Mosaic Smart Data Ltd, and for the last four years has been bringing the power of AI to bear at Wise, in a variety of domains, from fraud detection to trading algorithms and causal inference for A/B testing and marketing. Egor has multiple side projects, such as RL for molecular optimization, GenAI for generating and solving high school math problems, and others. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links https://github.com/transferwise/wise-pizza https://github.com/py-why/causaltune https://www.linkedin.com/posts/egorkraev_a-talk-on-experimentation-best-practices-activity-7092158531247755265-q0kt?utm_source=share&utm_medium=member_desktop --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Egor on LinkedIn: https://www.linkedin.com/in/egorkraev/ Timestamps:[00:00] Egor's preferred coffee[00:40] Takeaways[01:56] Swiss pirate party[04:08] Ghana Work and Wise[07:21] AI Bridge Unstructured Structured[12:40] AI in Wise Fintech[16:26] AI as Dag Node[19:38] Causal Inference with ML[25:35] Insight validation process[31:00] Agent UX and challenges[37:11] Monthly crew vision[41:00] Montley Crew Framework Abstraction[42:57] Causal tune[47:38] Wise email campaigns[50:29] Organizational structures in AI[53:19] AI uncertainty and hallucinations[55:08] Decentralized orgs structures[1:01:34] Permaculture and team synergy[1:03:29] Wrap up…
 
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Re-Platforming Your Tech Stack // Michelle Marie Conway & Andrew Baker // #281 51:14

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Re-Platforming Your Tech Stack // MLOps Podcast #281 with Michelle Marie Conway, Lead Data Scientist at Lloyds Banking Group and Andrew Baker, Data Science Delivery Lead at Lloyds Banking Group. // Abstract Lloyds Banking Group is on a mission to embrace the power of cloud and unlock the opportunities that it provides. Andrew, Michelle, and their MLOps team have been on a journey over the last 12 months to take their portfolio of circa 10 Machine Learning models in production and migrate them from an on-prem solution to a cloud-based environment. During the podcast, Michelle and Andrew share their reflections as well as some dos (and don’ts!) of managing the migration of an established portfolio. // Bio Michelle Marie ConwayMichelle is a Lead Data Scientist in the high-performance data science team at Lloyds Banking Group. With deep expertise in managing production-level Python code and machine learning models, she has worked alongside fellow senior manager Andrew to drive the bank's transition to the Google Cloud Platform. Together, they have played a pivotal role in modernising the ML portfolio in collaboration with a remarkable ML Ops team. Originally from Ireland and now based in London, Michelle blends her technical expertise with a love for the arts. Andrew Baker graduated from the University of Birmingham with a first-class honours degree in Mathematics and Music with a Year in Computer Science and joined Lloyds Banking Group on their Retail graduate scheme in 2015. Since 2021, Andrew has worked in the world of data, firstly in shaping the Retail data strategy and most recently as a Data Science Delivery Lead, growing and managing a team of Data Scientists and Machine Learning Engineers. He has built a high-performing team responsible for building and maintaining ML models in production for the Consumer Lending division of the bank. Andrew is motivated by the role that data science and ML can play in transforming the business and its processes, and is focused on balancing the power of ML with the need for simplicity and explainability that enables business users to engage with the opportunities that exist in this space and the demands of a highly regulated environment. // MLOps Swag/Merch https://shop.mlops.community/ // Related LinksWebsite: https://www.michelleconway.co.uk/ https://www.linkedin.com/pulse/artificial-intelligence-just-when-data-science-answer-andrew-baker-hfdge/https://www.linkedin.com/pulse/artificial-intelligence-conundrum-generative-ai-andrew-baker-qla7e/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Michelle on LinkedIn: https://www.linkedin.com/in/michelle--conway/ Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrew-baker-90952289 Timestamps:[00:00] Michelle and Andrew's preferred coffee[00:25] Takeaways[01:03] Please like, share, leave a review, and subscribe to our MLOps channels![01:26] 5B Cloud Investment![04:54] MLOps end-to-end process[06:21] ML model handoff evolution[09:14] ML project finalization questions[14:15] Cloud migration journey[17:48] Cloud Flexibility vs Rigidity[19:18] On-prem to Cloud Transition[23:02] Vertex AI vs Pointed Solutions[27:47] Standardizing Model Documentation[30:17] Cloud Optimization and Efficiency[35:47] Tech debt challenges overcome [42:11] Chaos engineering insights[46:11] MLOps platform team collaboration[51:02] Wrap up…
 
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Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 57:33

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Jineet Doshi is an award-winning Scientist, Machine Learning Engineer, and Leader at Intuit with over 7 years of experience. He has a proven track record of leading successful AI projects and building machine-learning models from design to production across various domains, which have impacted 100 million customers and significantly improved business metrics, leading to millions of dollars of impact. Holistic Evaluation of Generative AI Systems // MLOps Podcast #280 with Jineet Doshi, Staff AI Scientist or AI Lead at Intuit. // Abstract Evaluating LLMs is essential in establishing trust before deploying them to production. Even post-deployment, evaluation is essential to ensure LLM outputs meet expectations, making it a foundational part of LLMOps. However, evaluating LLMs remains an open problem. Unlike traditional machine learning models, LLMs can perform a wide variety of tasks, such as writing poems, Q&A, summarization, etc. This leads to the question of how do you evaluate a system with such broad intelligence capabilities? This talk covers the various approaches for evaluating LLMs, such as classic NLP techniques, red teaming, and newer ones like using LLMs as a judge, along with the pros and cons of each. The talk includes an evaluation of complex GenAI systems like RAG and Agents. It also covers evaluating LLMs for safety and security, and the need to have a holistic approach for evaluating these very capable models. // Bio Jineet Doshi is an award-winning AI Lead and Engineer with over 7 years of experience. He has a proven track record of leading successful AI projects and building machine learning models from design to production across various domains, which have impacted millions of customers and have significantly improved business metrics, leading to millions of dollars of impact. He is currently an AI Lead at Intuit, where he is one of the architects and developers of their Generative AI platform, which is serving Generative AI experiences for more than 100 million customers around the world. Jineet is also a guest lecturer at Stanford University as part of their building LLM Applications class. He is on the Advisory Board of the University of San Francisco’s AI Program. He holds multiple patents in the field, is on the steering committee of MLOps World Conference, and has also co-chaired workshops at top AI conferences like KDD. He holds a Master's degree from Carnegie Mellon University. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://www.intuit.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jineet on LinkedIn: https://www.linkedin.com/in/jineetdoshi/ Timestamps:[00:00] Jineet's preferred coffee[00:20] Takeaways[01:24] Please like, share, leave a review, and subscribe to our MLOps channels![01:36] LLM evaluation at scale[03:13] Challenges in GenAI evaluation[08:09] Eval products vs platforms[09:28] Evaluation methods for models[14:03] NLP evaluation techniques[25:06] LLM as a judge/jury[31:56] LLMs and pizza brainstorming[34:07] Cost per answer breakdown[38:29] Evaluating RAG systems[44:00] Testing with LLMs and humans[49:23] Evaluating AI use cases[54:19] AI workflow stress testing[55:40] Wrap up…
 
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Unleashing Unconstrained News Knowledge Graphs to Combat Misinformation // Robert Caulk // #279 1:15:24

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Robert Caulk is responsible for directing software development, enabling research, coordinating company projects, quality control, proposing external collaborations, and securing funding. He believes firmly in open-source, having spent 12 years accruing over 1000 academic citations building open-source software in domains such as machine learning, image analysis, and coupled physical processes. He received his Ph.D. from Université Grenoble Alpes, France, in computational mechanics. Unleashing Unconstrained News Knowledge Graphs to Combat Misinformation // MLOps Podcast #279 with Robert Caulk, Founder of Emergent Methods. // Abstract Indexing hundreds of thousands of news articles per day into a knowledge graph (KG) was previously impossible due to the strict requirement that high-level reasoning, general world knowledge, and full-text context *must* be present for proper KG construction. The latest tools now enable such general world knowledge and reasoning to be applied cost-effectively to high volumes of news articles. Beyond the low cost of processing these news articles, these tools are also opening up a new, controversial approach to KG building - unconstrained KGs. We discuss the construction and exploration of the largest news-knowledge-graph on the planet - hosted on an endpoint at AskNews.app. During the talk, we aim to highlight some of the sacrifices and benefits that go hand-in-hand with using the infamous unconstrained KG approach. We conclude the talk by explaining how knowledge graphs like these help to mitigate misinformation. We provide some examples of how our clients are using this graph, such as generating sports forecasts, generating better social media posts, generating regional security alerts, and combating human trafficking. // Bio Robert is the founder of Emergent Methods, where he directs research and software development for large-scale applications. He is currently overseeing the structuring of hundreds of thousands of news articles per day in order to build the best news retrieval API in the world: https://asknews.app . // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://emergentmethods.ai News Retrieval API: https://asknews.app --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Rob on LinkedIn: https://www.linkedin.com/in/rcaulk/ Timestamps: [00:00] Rob's preferred coffee [00:05] Takeaways [00:55] Please like, share, leave a review, and subscribe to our MLOps channels! [01:00] Join our Local Organizer Carousel! [02:15] Knowledge Graphs and ontology [07:43] Ontology vs Noun Approach [12:46] Ephemeral tools for efficiency [17:26] Oracle to PostgreSQL migration [22:20] MEM Graph life cycle [29:14] Knowledge Graph Investigation Insights [33:37] Fine-tuning and distillation of LLMs [39:28] DAG workflow and quality control [46:23] Crawling nodes with Phi 3 Llama [50:05] AI pricing risks and strategies [56:14] Data labeling and poisoning [58:34] API costs vs News latency [1:02:10] Product focus and value [1:04:52] Ensuring reliable information [1:11:01] Podcast transcripts as News [1:13:08] Ontology trade-offs explained [1:15:00] Wrap up…
 
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LLM Distillation and Compression // Guanhua Wang // #278 49:47

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Guanhua Wang is a Senior Researcher in DeepSpeed Team at Microsoft . Before Microsoft , Guanhua earned his Computer Science PhD from UC Berkeley. Domino: Communication-Free LLM Training Engine // MLOps Podcast #278 with Guanhua "Alex" Wang, Senior Researcher at Microsoft. // Abstract Given the popularity of generative AI, Large Language Models (LLMs) often consume hundreds or thousands of GPUs to parallelize and accelerate the training process. Communication overhead becomes more pronounced when training LLMs at scale. To eliminate communication overhead in distributed LLM training, we propose Domino, which provides a generic scheme to hide communication behind computation. By breaking the data dependency of a single batch training into smaller independent pieces, Domino pipelines these independent pieces of training and provides a generic strategy of fine-grained communication and computation overlapping. Extensive results show that compared with Megatron-LM, Domino achieves up to 1.3x speedup for LLM training on Nvidia DGX-H100 GPUs. // Bio Guanhua Wang is a Senior Researcher in the DeepSpeed team at Microsoft. His research focuses on large-scale LLM training and serving. Previously, he led the ZeRO++ project at Microsoft, which helped reduce over half of model training time inside Microsoft and LinkedIn. He also led and was a major contributor to Microsoft Phi-3 model training. He holds a CS PhD from UC Berkeley, advised by Prof Ion Stoica. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://guanhuawang.github.io/ DeepSpeed hiring: https://www.microsoft.com/en-us/research/project/deepspeed/opportunities/ Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference: https://youtu.be/cntxC3g22oU --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Guanhua on LinkedIn: https://www.linkedin.com/in/guanhua-wang/ Timestamps: [00:00] Guanhua's preferred coffee [00:17] Takeaways [01:36] Please like, share, leave a review, and subscribe to our MLOps channels! [01:47] Phi model explanation [06:29] Small Language Models optimization challenges [07:29] DeepSpeed overview and benefits [10:58] Crazy unimplemented crazy AI ideas [17:15] Post-training vs QAT [19:44] Quantization over distillation [24:15] Using Lauras [27:04] LLM scaling sweet spot [28:28] Quantization techniques [32:38] Domino overview [38:02] Training performance benchmark [42:44] Data dependency-breaking strategies [49:14] Wrap up…
 
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AI's Next Frontier // Aditya Naganath // #277 57:30

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Thanks to the High Signal Podcast by Delphina: https://go.mlops.community/HighSignalPodcast Aditya Naganath is an experienced investor currently working with Kleiner Perkins . He has a passion for connecting with people over coffee and discussing various topics related to tech, products, ideas, and markets. AI's Next Frontier // MLOps Podcast #277 with Aditya Naganath, Principal at Kleiner Perkins. // Abstract LLMs have ushered in an unmistakable supercycle in the world of technology. The low-hanging use cases have largely been picked off. The next frontier will be AI coworkers who sit alongside knowledge workers, doing work side by side. At the infrastructure level, one of the most important primitives invented by man - the data center- is being fundamentally rethought in this new wave. // Bio Aditya Naganath joined Kleiner Perkins’ investment team in 2022 with a focus on artificial intelligence, enterprise software applications, infrastructure, and security. Prior to joining Kleiner Perkins, Aditya was a product manager at Google, focusing on growth initiatives for the next billion users team. He previously was a technical lead at Palantir Technologies and formerly held software engineering roles at Twitter and Nextdoor, where he was a Kleiner Perkins fellow. Aditya earned a patent during his time at Twitter for a technical analytics product he co-created. Originally from Mumbai, India, Aditya graduated magna cum laude from Columbia University with a bachelor’s degree in Computer Science and an MBA from Stanford University. Outside of work, you can find him playing guitar with a hard rock band, competing in chess or on the squash courts, and fostering puppies. He is also an avid poker player. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Faith's Hymn by Beautiful Chorus: ⁠ ⁠https://open.spotify.com/track/1bDv6grQB5ohVFI8UDGvKK?si=4b00752eaa96413b⁠ ⁠ Substack: ⁠ ⁠https://adityanaganath.substack.com/?utm_source=substack&utm_medium=web&utm_campaign=substack_profile⁠ ⁠ With thanks to the High Signal Podcast by Delphina: https://go.mlops.community/HighSignalPodcast Building the Future of AI in Software Development // Varun Mohan // MLOps Podcast #195 - ⁠ ⁠https://youtu.be/1DJKq8StuTo⁠ ⁠ Do Re MI for Training Metrics: Start at the Beginning // Todd Underwood // AIQCON - ⁠ ⁠https://youtu.be/DxyOlRdCofo --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Aditya on LinkedIn: https://www.linkedin.com/in/aditya-naganath/ Timestamps:[00:00] Aditya's preferred coffee[00:07] Takeaways[01:33] Please like, share, leave a review, and subscribe to our MLOps channels![01:44] Investing in AI frenzy[02:23] Team dynamics insights[04:57] Evaluating fad companies[05:39] AI infrastructure and MLOps[07:58] Challenges in MLOps Standardization[08:59] ML vs Data platforms[14:08] LLMOps vs MLOps[17:52] Together vs Competitors[21:19] AI application areas[27:43] High Signal Podcast by Delphina Ad[28:36] AI co-pilot in coding[33:50] LLM providers overview[37:30] AI paradigms and competition[46:01] GPU failure management strategies[52:58] Inference drives cloud revenue[55:37] Wrap up…
 
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PyTorch for Control Systems and Decision Making // Vincent Moens // #276 56:39

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Dr Vincent Moens is an Applied Machine Learning Research Scientist at Meta and an author of TorchRL and TensorDict in Pytorch.PyTorch for Control Systems and Decision Making // MLOps Podcast #276 with Vincent Moens, Research Engineer at Meta. // Abstract PyTorch is widely adopted across the machine learning community for its flexibility and ease of use in applications such as computer vision and natural language processing. However, supporting reinforcement learning, decision-making, and control communities is equally crucial, as these fields drive innovation in areas like robotics, autonomous systems, and game-playing. This podcast explores the intersection of PyTorch and these fields, covering practical tips and tricks for working with PyTorch, an in-depth look at TorchRL, and discussions on debugging techniques, optimization strategies, and testing frameworks. By examining these topics, listeners will understand how to effectively use PyTorch for control systems and decision-making applications. // Bio Vincent Moens is a research engineer on the PyTorch core team at Meta, based in London. As the maintainer of TorchRL ( https://github.com/pytorch/rl ) and TensorDict ( https://github.com/pytorch/tensordict ), Vincent plays a key role in supporting the decision-making community within the PyTorch ecosystem. Alongside his technical role in the PyTorch community, Vincent also actively contributes to AI-related research projects. Before joining Meta, Vincent worked as an ML researcher at Huawei and AIG. Vincent holds a Medical Degree and a PhD in Computational Neuroscience. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Musical recommendation: https://open.spotify.com/artist/1Uff91EOsvd99rtAupatMP?si=jVkoFiq8Tmq0fqK_OIEglg Website: github.com/vmoens TorchRL: https://github.com/pytorch/rl TensorDict: https://github.com/pytorch/tensordict LinkedIn post: https://www.linkedin.com/posts/vincent-moens-9bb91972_join-the-tensordict-discord-server-activity-7189297643322253312-Wo9J?utm_source=share&utm_medium=member_desktop --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vincent on LinkedIn: https://www.linkedin.com/in/mvi/ Timestamps:[00:00] Vincent preferred coffee[00:12] Takeaways[01:03] PyTorch tips and tricks[05:30] Documentation Ambiguities and Unintended Guidance[10:34] Modern copies and trade-offs[17:56] Modular ML frameworks[21:01] RL abstraction and generalization[23:08] Developer Experience vs Functionality[29:22] Streamlining user workflows[31:10] Developer experience challenges[36:50] Torch logs and contributions[40:29] Well-formatted GitHub issue[43:13] Testing PyTorch models[48:45] Exciting PyTorch Development[51:49] Tool discovery and sharing[55:05] Wrap up…
 
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AI-Driven Code: Navigating Due Diligence & Transparency in MLOps // Matt van Itallie // #275 57:01

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Matt Van Itallie is the founder and CEO of Sema . Prior to this, they were the Vice President of Customer Support and Customer Operations at Social Solutions. AI-Driven Code: Navigating Due Diligence & Transparency in MLOps // MLOps Podcast #275 with Matt van Itallie, Founder and CEO of Sema. // Abstract Matt Van Itallie, founder and CEO of Sema, discusses how comprehensive codebase evaluations play a crucial role in MLOps and technical due diligence. He highlights the impact of Generative AI on code transparency and explains the Generative AI Bill of Materials (GBOM), which helps identify and manage risks in AI-generated code. This talk offers practical insights for technical and non-technical audiences, showing how proper diligence can enhance value and mitigate risks in machine learning operations. // Bio Matt Van Itallie is the Founder and CEO of Sema. He and his team have developed Comprehensive Codebase Scans, the most thorough and easily understandable assessment of a codebase and engineering organization. These scans are crucial for private equity and venture capital firms looking to make informed investment decisions. Sema has evaluated code within organizations that have a collective value of over $1 trillion. In 2023, Sema served 7 of the 9 largest global investors, along with market-leading strategic investors, private equity, and venture capital firms, providing them with critical insights. In addition, Sema is at the forefront of Generative AI Code Transparency, which measures how much code created by GenAI is in a codebase. They are the inventors behind the Generative AI Bill of Materials (GBOM), an essential resource for investors to understand and mitigate risks associated with AI-generated code. Before founding Sema, Matt was a Private Equity operating executive and a management consultant at McKinsey. He graduated from Harvard Law School and has had some interesting adventures, like hiking a third of the Appalachian Trail and biking from Boston to Seattle. Full bio: https://alistar.fm/bio/matt-van-itallie // MLOps Swag/Merch https://shop.mlops.community/ // Related LinksWebsite: https://en.m.wikipedia.org/wiki/Michael_Gschwind --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Matt on LinkedIn: https://www.linkedin.com/in/mvi/ Timestamps:[00:00] Matt's preferred coffee[00:07] Takeaways[01:25] Please like, share, leave a review, and subscribe to our MLOps channels![02:23] Code-based scans overview[09:16] FinOps Automation and Recommendations[12:03] Code quality evaluation layers[16:10] Bridging Tech-Biz gap[21:16] Measurable insights for leadership[29:09] Startup prioritization and metrics[32:53] GenAI Code Deletion Insights[37:07] AI vs Developer Expertise[41:47] GenAI Copyright Concerns[46:41] Open Source Risks AI[53:57] Code Defensibility and Acquisitions[56:19] Wrap up…
 
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PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274 57:44

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Dr. Michael Gschwind is a Director / Principal Engineer for PyTorch at Meta Platforms . At Meta , he led the rollout of GPU Inference for production services. // MLOps Podcast #274 with Michael Gschwind, Software Engineer, Software Executive at Meta Platforms. // Abstract Explore the role in boosting model performance, on-device AI processing, and collaborations with tech giants like ARM and Apple. Michael shares his journey from gaming console accelerators to AI, emphasizing the power of community and innovation in driving advancements. // Bio Dr. Michael Gschwind is a Director / Principal Engineer for PyTorch at Meta Platforms. At Meta, he led the rollout of GPU Inference for production services. He led the development of MultiRay and Textray, the first deployment of LLMs at a scale exceeding a trillion queries per day shortly after its rollout. He created the strategy and led the implementation of PyTorch donation optimization with Better Transformers and Accelerated Transformers, bringing Flash Attention, PT2 compilation, and ExecuTorch into the mainstream for LLMs and GenAI models. Most recently, he led the enablement of large language models on-device AI with mobile and edge devices. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://en.m.wikipedia.org/wiki/Michael_Gschwind --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Michael on LinkedIn: https://www.linkedin.com/in/michael-gschwind-3704222/?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app Timestamps: [00:00] Michael's preferred coffee [00:21] Takeaways [01:59] Please like, share, leave a review, and subscribe to our MLOps channels! [02:10] Gaming to AI Accelerators [11:34] Torch Chat goals [18:53] Pytorch benchmarking and competitiveness [21:28] Optimizing MLOps models [24:52] GPU optimization tips [29:36] Cloud vs On-device AI [38:22] Abstraction across devices [42:29] PyTorch developer experience [45:33] AI and MLOps-related antipatterns [48:33] When to optimize [53:26] Efficient edge AI models [56:57] Wrap up…
 
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LLMs to agents: The Beauty & Perils of Investing in GenAI // VC Panel // Agents in Production 33:24

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//Abstract In this segment, the Panel will dive into the evolving landscape of AI, where large language models (LLMs) power the next wave of intelligent agents. In this engaging panel, leading investors Meera (Redpoint), George (Sequoia), and Sandeep (Prosus Ventures) discuss the promise and pitfalls of AI in production. From transformative industry applications to the challenges of scalability, costs, and shifting business models, this session unpacks the metrics and insights shaping GenAI's future. Whether you're excited about AI's potential or wary of its complexities, this is a must-watch for anyone exploring the cutting edge of tech investment. //Bio Host: Paul van der Boor Senior Director Data Science @ Prosus Group Sandeep Bakshi Head of Investments, Europe @ Prosus Meera Clark Principal @ Redpoint Ventures George Robson Partner @ Sequoia Capital A Prosus | MLOps Community Production…
 
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We Can All Be AI Engineers and We Can Do It with Open Source Models // Luke Marsden // #273 51:08

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Luke Marsden , is a passionate technology leader. Experienced in consultant, CEO, CTO, tech lead, product, sales, and engineering roles. Proven ability to conceive and execute a product vision from strategy to implementation, while iterating on product-market fit. We Can All Be AI Engineers and We Can Do It with Open Source Models // MLOps Podcast #273 with Luke Marsden, CEO of HelixML. // Abstract In this podcast episode, Luke Marsden explores practical approaches to building Generative AI applications using open-source models and modern tools. Through real-world examples, Luke breaks down the key components of GenAI development, from model selection to knowledge and API integrations, while highlighting the data privacy advantages of open-source solutions. // Bio Hacker & entrepreneur. Founder at helix.ml. Career spanning DevOps, MLOps, and now LLMOps. Working on bringing business value to local, open-source LLMs. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related LinksWebsite: https://helix.ml About open source AI: https://blog.helix.ml/p/the-open-source-ai-revolution Ratatat Cream on Chrome: https://open.spotify.com/track/3s25iX3minD5jORW4KpANZ?si=719b715154f64a5f --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Luke on LinkedIn: https://www.linkedin.com/in/luke-marsden-71b3789/ Timestamps:[00:00] Michael's preferred coffee[00:21] Takeaways[01:59] Please like, share, leave a review, and subscribe to our MLOps channels![02:10] Gaming to AI Accelerators[11:34] Torch Chat goals[18:53] Pytorch benchmarking and competitiveness[21:28] Optimizing MLOps models[24:52] GPU optimization tips[29:36] Cloud vs On-device AI[38:22] Abstraction across devices [42:29] PyTorch developer experience[45:33] AI and MLOps-related antipatterns[48:33] When to optimize[53:26] Efficient edge AI models[56:57] Wrap up…
 
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Exploring AI Agents: Voice, Visuals, and Versatility // Panel // Agents in Production 28:58

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//Abstract This panel speaks about the diverse landscape of AI agents, focusing on how they integrate voice interfaces, GUIs, and small language models to enhance user experiences. They'll also examine the roles of these agents in various industries, highlighting their impact on productivity, creativity, and user experience and how these empower developers to build better solutions while addressing challenges like ensuring consistent performance and reliability across different modalities when deploying AI agents in production. //Bio Host: Diego Oppenheimer Co-founder @ Guardrails AI Jazmia Henry Founder and CEO @ Iso AI Rogerio Bonatti Researcher @ Microsoft Julia Kroll Applied Engineer @ Deepgram Joshua Alphonse Director of Developer Relations @ PremAI A Prosus | MLOps Community Production…
 
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The Impact of UX Research in the AI Space // Lauren Kaplan // #272 1:08:19

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Lauren Kaplan is a sociologist and writer. She earned her PhD in Sociology at Goethe University Frankfurt and worked as a researcher at the University of Oxford and UC Berkeley. The Impact of UX Research in the AI Space // MLOps Podcast #272 with Lauren Kaplan, Sr UX Researcher. // Abstract In this MLOps Community podcast episode, Demetrios and UX researcher Lauren Kaplan explore how UX research can transform AI and ML projects by aligning insights with business goals and enhancing user and developer experiences. Kaplan emphasizes the importance of stakeholder alignment, proactive communication, and interdisciplinary collaboration, especially in adapting company culture post-pandemic. They discuss UX’s growing relevance in AI, challenges like bias, and the use of AI in research, underscoring the strategic value of UX in driving innovation and user satisfaction in tech. // Bio Lauren is a sociologist and writer. She earned her PhD in Sociology at Goethe University Frankfurt and worked as a researcher at the University of Oxford and UC Berkeley. Passionate about homelessness and Al, Lauren joined UCSF and later Meta. Lauren recently led UX research at a global Al chip startup and is currently seeking new opportunities to further her work in UX research and AI. At Meta, Lauren led UX research for 1) Privacy-Preserving ML and 2) PyTorch. Lauren has worked on NLP projects such as Word2Vec analysis of historical HIV/AIDS documents presented at TextXD, UC Berkeley 2019. Lauren is passionate about understanding technology and advocating for the people who create and consume Al. Lauren has published over 30 peer-reviewed research articles in domains including psychology, medicine, sociology, and more.” // MLOps Swag/Merchhttps: mlops-community.myshopify.com/ // Related Links Podcast on AI UX https://open.substack.com/pub/aistudios/p/how-to-do-user-research-for-ai-products?r=7hrv8&utm_medium=ios 2024 State of AI Infra at Scale Research Report https://ai-infrastructure.org/wp-content/uploads/2024/03/The-State-of-AI-Infrastructure-at-Scale-2024.pdf Privacy-Preserving ML UX Public Article https://www.ttclabs.net/research/how-to-help-people-understand-privacy-enhancing-technologies Homelessness research and more: https://scholar.google.com/citations?user=24zqlwkAAAAJ&hl=en Agents in Production: https://home.mlops.community/public/events/aiagentsinprod Mk.gee Si (Bonus Track): https://open.spotify.com/track/1rukW2Wxnb3GGlY0uDWIWB?si=4d5b0987ad55444a --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Lauren on LinkedIn: https://www.linkedin.com/in/laurenmichellekaplan?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app Timestamps:[00:00] Lauren's Introduction[00:07] Join the AI Agents in Production Conference on November 13![01:26] Takeaways[02:13] Please like, share, leave a review, and subscribe to our MLOps channels![02:26] UX research overview[03:31] UX research methods[06:22] Effective interview strategies[10:33] Broader UX Understanding[14:42] Data Synthesis and Prioritization[20:28] Measuring Impact in ML[27:32] Phased Project Rollout[31:36] UXR in Startups vs Big Companies[40:32] AI Research Project Scope[48:03] Increasing UX Maturity[51:15] Career Paths in UX Research[55:51] UX Beyond Tech[59:31] Engineering User Research Tools[1:06:16] Wrap up…
 
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EU AI Act - Navigating New Legislation // Petar Tsankov // MLOps Podcast #271 58:56

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Dr. Petar Tsankov is a researcher and entrepreneur in the field of Computer Science and Artificial Intelligence (AI). EU AI Act - Navigating New Legislation // MLOps Podcast #271 with Petar Tsankov, Co-Founder and CEO of LatticeFlow AI. Big thanks to LatticeFlow for sponsoring this episode! // Abstract Dive into AI risk and compliance. Petar Tsankov, a leader in AI safety, talks about turning complex regulations into clear technical requirements and the importance of benchmarks in AI compliance, especially with the EU AI Act. We explore his work with big AI players and the EU on safer, compliant models, covering topics from multimodal AI to managing AI risks. He also shares insights on "Comply," an open-source tool for checking AI models against EU standards, making compliance simpler for AI developers. A must-listen for those tackling AI regulation and safety. // Bio Co-founder & CEO at LatticeFlow AI, building the world's first product enabling organizations to build performant, safe, and trustworthy AI systems. Before starting LatticeFlow AI, Petar was a senior researcher at ETH Zurich working on the security and reliability of modern systems, including deep learning models, smart contracts, and programmable networks. Petar has co-created multiple publicly available security and reliability systems that are regularly used: = ERAN, the world's first scalable verifier for deep neural networks: https://github.com/eth-sri/eran = VerX, the world's first fully automated verifier for smart contracts: https://verx.ch = Securify, the first scalable security scanner for Ethereum smart contracts: https://securify.ch = DeGuard, de-obfuscates Android binaries: http://apk-deguard.com = SyNET, the first scalable network-wide configuration synthesis tool: https://synet.ethz.ch Petar also co-founded ChainSecurity, an ETH spin-off that, within 2 years, became a leader in formal smart contract audits and was acquired by PwC Switzerland in 2020. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://latticeflow.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Petar on LinkedIn: https://www.linkedin.com/in/petartsankov/ Timestamps:[00:00] Petar's preferred coffee[00:13] Takeaways[01:12] AI Governance and EU Compliance[04:16] AI Governance and Model Management[09:48] AI Governance and Risk Management[11:38] COMPL-AI[15:02] EU AI Act Compliance Challenges[17:16] EU AI Act Actionability[22:26] Compliance and toxicity issues[25:48] Model benchmarking and metrics[28:13] Copyright and model evaluation[32:28] SOC AI certification[33:07] EU AI Act Gaps[37:05] Integrity, safety, and compliance[41:13] Benchmarking and Overfitting Concerns[43:15] Tiered compliance approaches[46:03] Bridging Law and Tech[48:00] Multimodal AI Feature[51:45] AI Risk and Mitigation[55:37] Future Directions for Multimodal Models[57:33] Wrap up…
 
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Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // Bernie Wu // #270 55:18

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Bernie Wu is VP of Business Development for MemVerge . He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies, including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft. Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // MLOps Podcast #270 with Bernie Wu, VP Strategic Partnerships/Business Development of MemVerge. // Abstract Limited memory capacity hinders the performance and potential of research and production environments utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques. This discussion explores how leveraging industry-standard CXL memory can be configured as a secondary, composable memory tier to alleviate this constraint. We will highlight some recent work we’ve done in integrating of this novel class of memory into LLM/RAG/vector database frameworks and workflows. Disaggregated shared memory is envisioned to offer high-performance, low-latency caches for model/pipeline checkpoints of LLM models, KV caches during distributed inferencing, LORA adaptors, and in-process data for heterogeneous CPU/GPU workflows. We expect to showcase these types of use cases in the coming months. // Bio Bernie is VP of Strategic Partnerships/Business Development for MemVerge. His focus has been building partnerships in the AI/ML, Kubernetes, and CXL memory ecosystems. He has 25+ years of experience as a senior executive for data center hardware and software infrastructure companies, including companies such as Conner/Seagate, Cheyenne Software, Trend Micro, FalconStor, Levyx, and MetalSoft. He is also on the Board of Directors for Cirrus Data Solutions. Bernie has a BS/MS in Engineering from UC Berkeley and an MBA from UCLA. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.memverge.com Accelerating Data Retrieval in Retrieval Augmentation Generation (RAG) Pipelines using CXL: https://memverge.com/accelerating-data-retrieval-in-rag-pipelines-using-cxl/ Do Re MI for Training Metrics: Start at the Beginning // Todd Underwood // AIQCON: https://youtu.be/DxyOlRdCofo Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // MLOps Podcast #228: https://youtu.be/6MY-IgqiTpg Compute Express Link (CXL) FPGA IP: https://www.intel.com/content/www/us/en/products/details/fpga/intellectual-property/interface-protocols/cxl-ip.htmlUltra Ethernet Consortium: https://ultraethernet.org/ Unified Acceleration (UXL) Foundation: https://www.intel.com/content/www/us/en/developer/articles/news/unified-acceleration-uxl-foundation.html RoCE networks for distributed AI training at scale: https://engineering.fb.com/2024/08/05/data-center-engineering/roce-network-distributed-ai-training-at-scale/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Bernie on LinkedIn: https://www.linkedin.com/in/berniewu/ Timestamps: [00:00] Bernie's preferred coffee [00:11] Takeaways [01:37] First principles thinking focus [05:02] Memory Abundance Concept Discussion [06:45] Managing load spikes [09:38] GPU checkpointing challenges [16:29] Distributed memory problem solving [18:27] Composable and Virtual Memory [21:49] Interactive chat annotation [23:46] Memory elasticity in AI [27:33] GPU networking tests [29:12] GPU Scheduling workflow optimization [32:18] Kubernetes Extensions and Tools [37:14] GPU bottleneck analysis [42:04] Economical memory strategies [45:14] Elastic memory management strategies [47:57] Problem-solving approach [50:15] AI infrastructure elasticity evolution [52:33] RDMA and RoCE explained [54:14] Wrap up…
 
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How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269 1:01:42

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Gideon Mendels is the Chief Executive Officer at Comet , the leading solution for managing machine learning workflows. How to Systematically Test and Evaluate Your LLMs Apps // MLOps Podcast #269 with Gideon Mendels, CEO of Comet. // Abstract When building LLM Applications, Developers need to take a hybrid approach from both ML and SW Engineering best practices. They need to define eval metrics and track their entire experimentation to see what is and is not working. They also need to define comprehensive unit tests for their particular use case so they can confidently check if their LLM App is ready to be deployed. // Bio Gideon Mendels is the CEO and co-founder of Comet, the leading solution for managing machine learning workflows from experimentation to production. He is a computer scientist, ML researcher and entrepreneur at his core. Before Comet, Gideon co-founded GroupWize, where they trained and deployed NLP models processing billions of chats. His journey with NLP and Speech Recognition models began at Columbia University and Google, where he worked on hate speech and deception detection. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related LinksWebsite: https://www.comet.com/site/ All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s Opik by Comet: https://www.comet.com/site/products/opik/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Gideon on LinkedIn: https://www.linkedin.com/in/gideon-mendels/ Timestamps: [00:00] Gideon's preferred coffee [00:17] Takeaways [01:50] A huge shout-out to Comet ML for sponsoring this episode! [02:09] Please like, share, leave a review, and subscribe to our MLOps channels! [03:30] Evaluation metrics in AI [06:55] LLM Evaluation in Practice [10:57] LLM testing methodologies [16:56] LLM as a judge [18:53] OPIC track function overview [20:33] Tracking user response value [26:32] Exploring AI metrics integration [29:05] Experiment tracking and LLMs [34:27] Micro Macro collaboration in AI [38:20] RAG Pipeline Reproducibility Snapshot [40:15] Collaborative experiment tracking [45:29] Feature flags in CI/CD [48:55] Labeling challenges and solutions [54:31] LLM output quality alerts [56:32] Anomaly detection in model outputs [1:01:07] Wrap up…
 
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Exploring the Impact of Agentic Workflows // Raj Rikhy // #268 51:02

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Raj Rikhy is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce. // MLOps Podcast #268 with Raj Rikhy, Principal Product Manager at Microsoft. // Abstract In this MLOps Community podcast, Demetrios chats with Raj Rikhy, Principal Product Manager at Microsoft, about deploying AI agents in production. They discuss starting with simple tools, setting clear success criteria, and deploying agents in controlled environments for better scaling. Raj highlights real-time uses like fraud detection and optimizing inference costs with LLMs, while stressing human oversight during early deployment to manage LLM randomness. The episode offers practical advice on deploying AI agents thoughtfully and efficiently, avoiding over-engineering, and integrating AI into everyday applications. // Bio Raj is a Senior Product Manager at Microsoft AI + R, enabling deep reinforcement learning use cases for autonomous systems. Previously, Raj was the Group Technical Product Manager in the CDO for Data Science and Deep Learning at IBM. Prior to joining IBM, Raj has been working in product management for several years - at Bitnami, Appdirect, and Salesforce. // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.microsoft.com/en-us/research/focus-area/ai-and-microsoft-research/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Raj on LinkedIn: https://www.linkedin.com/in/rajrikhy/ Timestamps: [00:00] Raj's preferred coffee [00:16] Takeaways [00:23] Join the AI Agents in Production Conference on November 13th! [01:25] Categorizing different agents [06:59] Agent environment frameworks [15:52] Debugging Strategies for Complex Systems [22:26] Evaluating Agent Frameworks Effectively [28:30] Defining success in projects [31:45] Process simplification benefits [35:32] Agent workflow use cases [39:29] Tinder for clothing recommendation [44:20] Speed Reliability Trade-offs in ML [48:06] Brilliant minds and doubts [48:50] Wrap up…
 
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The Only Constant is (Data) Change // Panel // DE4AI 40:49

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//Abstract If there is one thing that is true, it is data is constantly changing. How can we keep up with these changes? How can we make sure that every stakeholder has visibility? How can we create a culture of understanding around data change management? //Bio - Benjamin Rogojan: Data Science And Engineering Consultant @ Seattle Data Guy - Chad Sanderson: CEO & Co-Founder @ Gable - Christophe Blefari: CTO & Co-founder @ NAO - Maggie Hays: Founding Community Product Manager, DataHub @ Acryl Data A big thank you to our Premium Sponsors @Databricks , @tecton8241 , & @onehouseHQ for their generous support!…
 
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The AI Dream Team: Strategies for ML Recruitment and Growth // Jelmer Borst and Daniela Solis // #267 58:42

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The AI Dream Team: Strategies for ML Recruitment and Growth // MLOps Podcast #267 with Jelmer Borst, Analytics & Machine Learning Domain Lead, and Daniela Solis, Machine Learning Product Owner, of Picnic. // Abstract Like many companies, Picnic started out with a small, central data science team. As this grows larger, focusing on more complex models, it questions the skillsets & organisational setup. Use an ML platform, or build ourselves? A central team vs. embedded ? Hire data scientists vs. ML engineers vs. MLOps engineers. How to foster a team culture of end-to-end ownership to balance short-term & long-term impact // Bio Jelmer Borst Jelmer leads the analytics & machine learning teams at Picnic, an app-only online groceries company based in the Netherlands. Whilst his background is in aerospace engineering, he was looking for something faster-paced and found that at Picnic. He loves the intersection of solving business challenges using technology & data. In his free time loves to cook food and tinker with the latest AI developments. Daniela Solis Morales As a Machine Learning Lead at Picnic, I am responsible for ensuring the success of end-to-end Machine Learning systems. My work involves bringing models into production across various domains, including Personalization, Fraud Detection, and Natural Language Processing. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jelmer on LinkedIn: https://www.linkedin.com/in/japborst Connect with Daniela on LinkedIn: https://www.linkedin.com/in/daniela-solis-morales/ Timestamps: [00:00] Jelmer and Daniela's preferred coffee [00:37] Takeaways [03:46] Please like, share, leave a review, and subscribe to our MLOps channels! [03:58] Use case evolution review [08:24] Centralized ML strategy [11:53] Managing zombie models effectively [15:52] Clean data and collaboration [21:07] Snowflake ML Integration options [22:49] MLOps infrastructure components [25:36] Pull vs. Push Adoption [27:03] ML Model Monitoring Roles [31:56] Inventory prediction [36:00] Scaling machine learning teams [42:09] Team expansion and structure [48:20] Exploring effective team organization [51:43] Blog reading insights [54:25] Playing hard mode [57:33] Wrap up…
 
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Making Your Company LLM-native // Francisco Ingham // #266 57:54

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Francisco Ingham , LLM consultant, NLP developer, and founder of Pampa Labs .Making Your Company LLM-native // MLOps Podcast #266 with Francisco Ingham, Founder of Pampa Labs. // Abstract Being an LLM-native is becoming one of the key differentiators among companies in vastly different verticals. Everyone wants to use LLMs, and everyone wants to be on top of the current tech, but what does it really mean to be LLM-native? LLM-native involves two ends of a spectrum. On the one hand, we have the product or service that the company offers, which surely offers many automation opportunities. LLMs can be applied strategically to scale at a lower cost and offer a better experience for users. But being LLM-native not only involves the company's customers, it also involves each stakeholder involved in the company's operations. How can employees integrate LLMs into their daily workflows? How can we, as developers, leverage the advancements in the field not only as builders but as adopters? We will tackle these and other key questions for anyone looking to capitalize on the LLM wave, prioritizing real results over the hype. // Bio Currently working at Pampa Labs, where we help companies become AI-native and build AI-native products. Our expertise lies on the LLM-science side, or how to build a successful data flywheel to leverage user interactions to continuously improve the product. We also spearhead Pampa-friends - the first Spanish-speaking community of AI Engineers. Previously worked in management consulting, was a TA in fastai in SF, and led the cross-AI + dev tools team at Mercado Libre. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: pampa.ai --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Francisco on LinkedIn: https://www.linkedin.com/in/fpingham/ Timestamps: [00:00] Francisco's preferred coffee [00:13] Takeaways [00:37] Please like, share, leave a review, and subscribe to our MLOps channels! [00:51] A Literature Geek [02:41] LLM-native company [03:54] Integrating LLM in workflows [07:21] Unexpected LLM applications [10:38] LLMs in the development process [14:00] Vibe check to evaluation [15:36] Experiment tracking optimizations [20:22] LLMs as judges discussion [24:43] Presentaciones automatizadas para podcast [27:48] AI operating system and agents [31:29] Importance of SEO expertise [35:33] Experimentation and evaluation [39:20] AI integration strategies [41:50] RAG approach spectrum analysis [44:40] Search vs Retrieval in AI [49:02] Recommender Systems vs RAG [52:08] LLMs in recommender systems [53:10] LLM interface design insights…
 
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Unpacking 3 Types of Feature Stores // Simba Khadder // #265 1:07:42

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Simba Khadder is the Founder & CEO of Featureform . He started his ML career in recommender systems, where he architected a multi-modal personalization engine that powered 100s of millions of users’ experiences. Unpacking 3 Types of Feature Stores // MLOps Podcast #265 with Simba Khadder, Founder & CEO of Featureform. // Abstract Simba dives into how feature stores have evolved and how they now intersect with vector stores, especially in the world of machine learning and LLMs. He breaks down what embeddings are, how they power recommender systems, and why personalization is key to improving LLM prompts. Simba also sheds light on the difference between feature and vector stores, explaining how each plays its part in making ML workflows smoother. Plus, we get into the latest challenges and cool innovations happening in MLOps. // Bio Simba Khadder is the Founder & CEO of Featureform. After leaving Google, Simba founded his first company, TritonML. His startup grew quickly, and Simba and his team built ML infrastructure that handled over 100M monthly active users. He instilled his learnings into Featureform’s virtual feature store. Featureform turns your existing infrastructure into a Feature Store. He’s also an avid surfer, a mixed martial artist, a published astrophysicist for his work on finding Planet 9, and he ran the SF marathon in basketball shoes. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: featureform.com BigQuery Feature Store // Nicolas Mauti // MLOps Podcast #255: https://www.youtube.com/watch?v=NtDKbGyRHXQ&ab_channel=MLOps.community --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Simba on LinkedIn: https://www.linkedin.com/in/simba-k/ Timestamps: [00:00] Simba's preferred coffee [00:08] Takeaways [02:01] Coining the term 'Embedding' [07:10] Dual Tower Recommender System [10:06] Complexity vs Reliability in AI [12:39] Vector Stores and Feature Stores [17:56] Value of Data Scientists [20:27] Scalability vs Quick Solutions [23:07] MLOps vs LLMOps Debate [24:12] Feature Stores' current landscape [32:02] ML lifecycle challenges and tools [36:16] Feature Stores bundling impact [42:13] Feature Stores and BigQuery [47:42] Virtual vs Literal Feature Store [50:13] Hadoop Community Challenges [52:46] LLM data lifecycle challenges [56:30] Personalization in prompting usage [59:09] Contextualizing company variables [1:03:10] DSPy framework adoption insights [1:05:25] Wrap up…
 
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Reinvent Yourself and Be Curious // Stefano Bosisio // MLOps Podcast #264 57:15

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Stefano Bosisio is an accomplished MLOps Engineer with a solid background in Biomedical Engineering, focusing on cellular biology, genetics, and molecular simulations. Reinvent Yourself and Be Curious // MLOps Podcast #264 with Stefano Bosisio, MLOps Engineer at Synthesia. // Abstract This talk goes through Stefano's experience, to be an inspirational source for whoever wants to jump into a career in the MLOps sector. Moreover, Stefano will also introduce his MLOps Course on the MLOps community platform. // Bio Stefano Bosisio is an MLOps Engineer with a versatile background that ranges from biomedical engineering to computational chemistry and data science. Stefano got an MSc in biomedical engineering from the Polytechnic of Milan, focusing on cellular biology, genetics, and molecular simulations. Then, he landed in Scotland, in Edinburgh, to earn a PhD in chemistry from the University of Edinburgh, where he developed robust physical theories and simulation methods to understand and unlock the drug discovery problem. After completing his PhD, Stefano transitioned into Data Science, where he began his career as a data scientist. His interest in machine learning engineering grew, leading him to specialize in building ML platforms that drive business success. Stefano's expertise bridges the gap between complex scientific research and practical machine learning applications, making him a key figure in the MLOps field. Bonus points beyond data: Stefano, as a proper Italian, loves cooking and (mainly) baking, playing the piano, crocheting, and running half-marathons. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://medium.com/@stefanobosisio1First MLOps Stack Course: https://learn.mlops.community/courses/languages/your-first-mlops-stack/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stefano on LinkedIn: https://www.linkedin.com/in/stefano-bosisio1/ Timestamps: [00:00] Stephano's preferred coffee [00:12] Takeaways [01:06] Stephano's MLOps Course [01:47] From Academia to AI Industry [09:10] Data science and platforms [16:53] Persistent MLOps challenges [21:23] Internal evangelization for success [24:21] Adapt communication skills to diverse individual needs [29:43] Key components of ML pipelines are essential [33:47] Create a generalizable AI training pipeline with Kubeflow [35:44] Consider cost-effective algorithms and deployment methods [39:02] Agree with Dream Platform; LLMs require a simple microservice [42:48] Auto scaling: crucial, tricky, prone to issues [46:28] Auto-scaling issues with Apache Beam data pipelines [49:49] Guiding students through MLOps with practical experience [53:16] Bulletproof Problem Solving: Decision trees for problem analysis [55:03] Evaluate tools critically; appreciate educational opportunities [57:01] Wrap up…
 
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Global Feature Store // Gottam Sai Bharath & Cole Bailey // #263 50:18

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Global Feature Store: Optimizing Locally and Scaling Globally at Delivery Hero // MLOps Podcast #263 with Delivery Hero's Gottam Sai Bharath, Senior Machine Learning Engineer & Cole Bailey, ML Platform Engineering Manager. // Abstract Delivery Hero innovates locally within each department to develop MLOps practices most effective in that particular context. We also discuss our efforts to reduce redundancy and inefficiency across the company. Hear about our experiences in creating multiple micro feature stores within our departments, and our goal to unify these into a Global Feature Store that is more powerful when combined. // Bio Sai Bharath Gottam With a passion for translating complex technical concepts into practical solutions, Sai excels at making intricate topics accessible and engaging. As a Senior Machine Learning Engineer at Delivery Hero, Sai works on cutting-edge machine learning platforms that guarantee seamless delivery experiences. Always eager to share insights and innovations, Sai is committed to making technology understandable and enjoyable for all. Cole Bailey Bridging data science and production-grade software engineering. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.deliveryhero.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sai on LinkedIn: https://www.linkedin.com/in/sai-bharath-gottam/ Connect with Cole on LinkedIn: www.linkedin.com/in/cole-bailey Timestamps: [00:00] Sai and Cole's preferred coffee [00:42] Takeaways [01:51] Please like, share, leave a review, and subscribe to our MLOps channels! [02:08] Life changes in Delivery Hero [05:21] Global Feature Store and Pandora [12:21] Tech integration strategies [20:08] Defining Feature and Feature Store [22:46] Feature Store vs Data Platform [26:26] Features are discoverable [32:56] Onboarding and Feature Testing [36:00] Data consistency [41:07] Future Vision Feature Store [44:17] Multi-cloud strategies [46:33] Wrap up…
 
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RAG Quality Starts with Data Quality // Adam Kamor // #262 59:33

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Adam Kamor is the Co-founder of Tonic , a company that specializes in creating mock data that preserves secure datasets. RAG Quality Starts with Data Quality // MLOps Podcast #262 with Adam Kamor, Co-Founder & Head of Engineering of Tonic.ai. // Abstract Dive into what makes Retrieval-Augmented Generation (RAG) systems tick—and it all starts with the data. We’ll be talking with an expert in the field who knows exactly how to transform messy, unstructured enterprise data into high-quality fuel for RAG systems. Expect to learn the essentials of data prep, uncover the common challenges that can derail even the best-laid plans, and discover some insider tips on how to boost your RAG system’s performance. We’ll also touch on the critical aspects of data privacy and governance, ensuring your data stays secure while maximizing its utility. If you’re aiming to get the most out of your RAG systems or just curious about the behind-the-scenes work that makes them effective, this episode is packed with insights that can help you level up your game. // Bio Adam Kamor, PhD, is the Co-founder and Head of Engineering of Tonic.ai. Since completing his PhD in Physics at Georgia Tech, Adam has committed himself to enabling the work of others through the programs he develops. In his roles at Microsoft and Kabbage, he handled UI design and led the development of new features to anticipate customer needs. At Tableau, he played a role in developing the platform’s analytics/calculation capabilities. As a founder of Tonic.ai, he is leading the development of unstructured data solutions that are transforming the work of fellow developers, analysts, and data engineers alike. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.tonic.ai Various topics about RAG and LLM security are available on Tonic.ai's blogs: https://www.tonic.ai/blog https://www.tonic.ai/blog/how-to-prevent-data-leakage-in-your-ai-applications-with-tonic-textual-and-snowpark-container-services https://www.tonic.ai/blog/rag-evaluation-series-validating-the-rag-performance-of-the-openais-rag-assistant-vs-googles-vertex-search-and-conversation https://www.youtube.com/watch?v=5xdyt4oRONU https://www.tonic.ai/blog/what-is-retrieval-augmented-generation-the-benefits-of-implementing-rag-in-using-llms --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Adam on LinkedIn: https://www.linkedin.com/in/adam-kamor-85720b48/ Timestamps: [00:00] Adam's preferred coffee [00:24] Takeaways [00:59] Huge shout-out to Tonic.ai for supporting the community! [01:03] Please like, share, leave a review, and subscribe to our MLOps channels! [01:18] Naming a product [03:38] Tonic Textual [08:00] Managing PII and Data Safety [10:16] Chunking strategies for context [14:19] Data prep for RAG [17:20] Data quality in AI systems [20:58] Data integrity in PDFs [27:12] Ensuring chatbot data freshness [33:02] Managed PostgreSQL and Vector DB [34:49] RBAC database vs file access [37:35] Slack AI data leakage solutions [42:26] Hot swapping [46:06] LLM security concerns [47:03] Privacy management best practices [49:02] Chatbot design patterns [50:39] RAG growth and impact [52:40] Retrieval Evaluation best practices [59:20] Wrap up…
 
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Who's MLOps for Anyway? // Jonathan Rioux // #261 1:10:14

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Jonathan Rioux is a Managing Principal of AI Consulting for EPAM Systems , where he advises clients on how to get from idea to realized AI products with the minimum of fuss and friction. Who's MLOps for Anyway? // MLOps Podcast #261 with Jonathan Rioux, Managing Principal, AI Consulting at EPAM Systems. // Abstract The year is 2024, and we are all staring into the cliff towards the abyss of disillusionment for Generative AI. Every organization, developer, and AI-adjacent individual is now talking about "making AI real" and "turning an ROI on AI initiatives". MLOps and LLMOps are taking the stage as the solution; equip your AI teams with the best tools money can buy, grab tokens by the fistful, and look at value raking in. Sounds familiar and eerily similar to the previous ML hype cycles? From solo devs to large organizations, how can we avoid the same pitfalls as last time and get out of the endless hamster wheel? // Bio Jonathan is a Managing Principal of AI Consulting for EPAM, where he advises clients on how to get from idea to realized AI products with the minimum of fuss and friction. He's obsessed with the mental models of ML and how to organize harmonious AI practices. Jonathan published "Data Analysis with Python and PySpark" (Manning, 2022). // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: raiks.ca --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jonathan on LinkedIn: https://www.linkedin.com/in/jonathanrx/ Timestamps: [00:00] Jonathan's preferred coffee [00:25] Takeaways [01:44] MLOps as not being sexy [03:49] Do not conflate MLOps with ROI [06:21] ML Certification Business Idea [11:02] AI Adoption Missteps [15:40] Slack AI Privacy Risks [18:17] Decentralized AI success [22:00] Michelangelo Hub-Spoke Model [27:45] Engineering tools for everyone [33:38 - 35:20] SAS Ad [35:21] POC to ROI transition [42:08] Repurposing project learnings [46:24] Balancing Innovation and ROI [55:35] Using classification model [1:00:24] Chatbot evolution comparison [1:01:20] Balancing Automation and Trust [1:06:30] Manual to AI transition [1:09:57] Wrap up…
 
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Alignment is Real // Shiva Bhattacharjee // #260 40:20

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Shiva Bhattacharjee is the Co-founder and CTO of TrueLaw , where we are building bespoke models for law firms for a wide variety of tasks. Alignment is Real // MLOps Podcast #260 with Shiva Bhattacharjee, CTO of TrueLaw Inc. // Abstract If the off-the-shelf model can understand and solve a domain-specific task well enough, either your task isn't that nuanced or you have achieved AGI. We discuss when fine-tuning is necessary over prompting and how we have created a loop of sampling - collecting feedback - and fine-tuning to create models that seem to perform exceedingly well in domain-specific tasks. // Bio 20 years of experience in distributed and data-intensive systems spanning work at Apple, Arista Networks, Databricks, and Confluent. Currently CTO at TrueLaw, where we provide a framework to fold in user feedback, such as lawyer critiques of a given task, and fold them into proprietary LLM models through fine-tuning mechanics, resulting in 7-10x improvements over the base model. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.truelaw.ai --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Shiva on LinkedIn: https://www.linkedin.com/in/shivabhattacharjee/ Timestamps: [00:00] Shiva's preferred coffee [00:58] Takeaways [01:17] DSPy Implementation [04:57] Evaluating DSPy risks [08:13] Community-driven DSPy tool [12:19] RAG implementation strategies [17:02] Cost-effective embedding fine-tuning [18:51] AI infrastructure decision-making [24:13] Prompt data flow evolution [26:32] Buy vs build decision [30:45] Tech stack insights [38:20] Wrap up…
 
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Ax a New Way to Build Complex Workflows with LLMs // Vikram Rangnekar // #259 53:25

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Vikram Rangnekar is an open-source software developer focused on simplifying LLM integration. He created LLMClient, a TypeScript library inspired by Stanford's DSP paper. With years of experience building complex LLM workflows, he previously worked as a senior software engineer at LinkedIn on Ad Serving. Ax a New Way to Build Complex Workflows with LLMs // MLOps Podcast #259 with Vikram Rangnekar, Software Engineer at Stealth. // Abstract Ax is a new way to build complex workflows with LLMs. It's a TypeScript library based on research done in the Stanford DSP paper. Concepts such as prompt signatures, prompt tuning, and composable prompts help you build RAG and agent-powered ideas that have, till now, been hard to build and maintain. Ax is designed for production usage. // Bio Vikram builds open-source software. Currently working on making it easy to build with LLMs. Created Ax, a TypeScript library that abstracts over all the complexity of LLMs, which is based on the research done in the Stanford DSP paper. Worked extensively with LLMs over the last few years to build complex workflows. Previously worked as a senior software engineer with LinkedIn on Ad Serving. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links The unofficial DSPy framework. Build LLM-powered Agents and "Agentic workflows" based on the Stanford DSP paper: https://axllm.dev All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vikram on LinkedIn: https://www.linkedin.com/in/vikramr Timestamps: [00:00] Vikram preferred coffee [00:41] Takeaways [01:05] Data Engineering for AI/ML Conference Ad [01:41] Vikram's work these days [04:54] Fine-tuned Model insights [06:22] JavaScript tool evolution [16:14] DSP knowledge distillation [17:34] DSP vs Manual examples [22:53] Optimizing task context [27:58] API type validation explained [30:25] LLM value and innovation [34:22] Navigating complex systems [37:30] DSP code generators explained [40:56] Exploring LLM personas [42:45] Optimizing small agents [43:32] Complex task assistance [49:53] Wrap up…
 
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Building in Production Human-centred GenAI Solutions // Mohamed Abusaid & Mara Pometti// #177 1:02:42

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MLOps Coffee Sessions #177 with Mohamed Abusaid and Mara Pometti, Building in Production Human-centred GenAI Solutions sponsored by QuantumBlack, AI by McKinsey . // Abstract Trust is paramount in the adoption of new technologies, especially in the realm of education. Mohamed and Mara shed light on the importance of AI governance programs and establishing AI governance boards to ensure safe and ethical use of technology while managing associated risks. They discuss the impact on customers, potential risks, and mitigation strategies that organizations must consider to protect their brand reputation and comply with regulations. // Bio Mara Pometti Mara is an Associate Design Director at McKinsey & Company, where she helps organisations drive AI adoption through human-centered methods. She defines herself as a data-savvy humanist. Her practice spans across AI, data journalism, and design with the overarching objective of finding the strategic intersection between AI models and human intents to implement responsible AI systems that move organisations forward. Previously, she led the AI Strategy practice at IBM, where she also developed the company’s first-ever data storytelling program. Yet, by background, she is a data journalist. She worked as a data journalist for agencies and newsrooms like Aljazeera. Mara lectured at many universities about how to humanize AI, including the London School of Economics. Her books and writing explore how to weave a humanistic approach to AI development. Mohamed Abusaid Am Mohamed, a tech enthusiast, hacker, avid traveler, and foodie all rolled into one individual. Built his first website when he was 9 and fell in love with computers and the internet ever since. Graduated with computer science from university although dabbled in electrical, electronic, and network engineering before that. When he's not reading up on the latest tech conversations and products on Hacker News, Mohamed spends his time traveling to new destinations and exploring their cuisine and culture. Mohamed works with different companies helping them tackle challenges in developing, deploying, and scaling their analytics to reach its potential. Some topics he's enthusiastic about include MLOps, DataOps, GenerativeAI, Product thinking, and building cross-functional teams to deliver user-first products. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links QuantumBlack, AI by McKinsey: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/ Connect with Mara on LinkedIn: https://www.linkedin.com/in/mara-pometti Connect with Mohamed on LinkedIn: https://www.linkedin.com/in/mabusaid/…
 
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Visualize - Bringing Structure to Unstructured Data // Markus Stoll // #258 50:38

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Markus Stoll is the Co-Founder of Renumics and the developer behind the open-source interactive ML dataset exploration tool, Spotlight. He shares insights on: AI in Engineering and Manufacturing Interactive ML Data Visualization ML Data Exploration Follow Markus for hands-on articles about leveraging ML while keeping a strong focus on data. Visualize - Bringing Structure to Unstructured Data // MLOps Podcast #258 with Markus Stoll, CTO of Renumics. A huge thank you to SAS for their generous support! // Abstract This talk is about how data visualization and embeddings can support you in understanding your machine-learning data. We explore methods to structure and visualize unstructured data like text, images, and audio for applications ranging from classification and detection to Retrieval-Augmented Generation. By using tools and techniques like UMAP to reduce data dimensions and visualization tools like Renumics Spotlight, we aim to make data analysis for ML easier. Whether you're dealing with interpretable features, metadata, or embeddings, we'll show you how to use them all together to uncover hidden patterns in multimodal data, evaluate the model performance for data subgroups, and find failure modes of your ML models. // Bio Markus Stoll began his career in the industry at Siemens Healthineers, developing software for the Heavy Ion Therapy Center in Heidelberg. He learned about software quality while developing a treatment machine weighing over 600 tons. He earned a Ph.D., focusing on combining biomechanical models with statistical models, through which he learned how challenging it is to bridge the gap between research and practical application in the healthcare domain. Since co-founding Renumics, he has been active in the field of AI for Engineering, e.g., AI for Computer Aided Engineering (CAE), implementing projects, contributing to their open-source library for data exploration for ML datasets (Renumics Spotlight), and writing articles about data visualization. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://renumics.com/ MLSecOps Community: https://community.mlsecops.com/ Blogs: https://towardsdatascience.com/visualize-your-rag-data-evaluate-your-retrieval-augmented-generation-system-with-ragas-fc2486308557 : https://medium.com/itnext/how-to-explore-and-visualize-ml-data-for-object-detection-in-images-88e074f46361 --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Markus on LinkedIn: https://www.linkedin.com/in/markus-stoll-b39a42138/ Timestamps: [00:00] Markus' preferred coffee [00:15] Takeaways [01:41] Please like, share, leave a review, and subscribe to our MLOps channels! [01:50] Register for the Data Engineering for AI/ML Conference now! [02:27] Current focus and updates [04:43] 3D Embeddings Visualization Explained [07:07] Question Embeddings vs Retrieval [08:24] Using heat maps effectively [10:30] User insights visualization RAG [16:59] 3D Crash Simulation Analysis [20:33] Simulation purpose clarification [22:34] Evaluating test data use cases [24:22] Real-world car testing [29:48] Identifying data issues early [33:33] Multimodal data integration [37:42] Custom vs Fine-tuned models [39:45] Data processing challenges [45:58] Use case-driven MVP [48:26 - 50:08] SAS Ad [50:09] Wrap up…
 
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AI Testing Highlights // Special MLOps Podcast Episode 9:54

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MLOps for GenAI Applications // Special MLOps Podcast episode with Demetrios Brinkmann, Chief Happiness Engineer at MLOps Community. // Abstract Demetrios explores common themes in ML model testing with insights from Erica Greene (Yahoo News), Matar Haller (ActiveFence), Mohamed Elgendy (Kolena), and Catherine Nelson (Freelance Data Scientist). They discuss tiered test cases, functional testing for hate speech, differences between AI and traditional software testing, and the complexities of evaluating LLMs. Demetrios wraps up by inviting feedback and promoting an upcoming virtual conference on data engineering for AI and ML. // Bio At the moment, Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps Community Podcasts. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Balancing Speed and Safety // Panel // AIQCON - https://youtu.be/c81puRgu3Kw AI For Good - Detecting Harmful Content at Scale // Matar Haller // MLOps Podcast #246 - https://youtu.be/wLKlZ6yHg1k What is AI Quality? // Mohamed Elgendy // MLOps Podcast #229 - https://youtu.be/-Jdmq4DiOew All Data Scientists Should Learn Software Engineering Principles // Catherine Nelson // Podcast #245 - https://youtu.be/yP6Eyny7p20 --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Timestamps: [00:00] Exploring common themes in MLOps community [00:49] Common patterns about model output and testing [01:34] Tiered test case strategy [03:05] Functional testing for models [05:24] Testing coverage and quality [07:47] Evaluating LLMs challenges [08:35] Please like, share, leave a review, and subscribe to our MLOps channels!…
 
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MLSecOps is Fundamental to Robust AISPM // Sean Morgan // #257 42:35

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Sean Morgan is an active open-source contributor and maintainer and is the special interest group lead for TensorFlow Addons. Learn more about the platform for end-to-end AI Security at https://protectai.com/ . MLSecOps is Fundamental to Robust AI Security Posture Management (AISPM) // MLOps Podcast #257 with Sean Morgan, Chief Architect at Protect AI. // Abstract MLSecOps, which is the practice of integrating security practices into the AIML lifecycle (think infusing MLOps with DevSecOps practices), is a critical part of any team’s AI Security Posture Management. In this talk, we’ll discuss how to threat model realistic AIML security risks, how you can measure your organization’s AI Security Posture, and most importantly, how you can improve that security posture through the use of MLSecOps. // Bio Sean Morgan is the Chief Architect at Protect AI. In prior roles, he's led production AIML deployments in the semiconductor industry, evaluated adversarial machine learning defenses for DARPA research programs, and most recently scaled customers on interactive machine learning solutions at AWS. In his free time, Sean is an active open-source contributor and maintainer and is the special interest group lead for TensorFlow Addons. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Sean's GitHub: https://github.com/seanpmorgan MLSecOps Community: https://community.mlsecops.com/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Sean on LinkedIn: https://www.linkedin.com/in/seanmorgan/ Timestamps: [00:00] Sean's preferred coffee [00:10] Takeaways [01:39] Register for the Data Engineering for AI/ML Conference now! [02:21] KubeCon Paris: Emphasis on security and AI [05:00] Concern about malicious data during the training process [09:29] Model builders, security, pulling foundational models, nuances [12:13] Hugging Face research on security issues [15:00] Inference servers exposed; potential for attack [19:45] Balancing ML and security processes for ease [23:23] Model artifact security in enterprise machine learning [25:04] Scanning models and datasets for vulnerabilities [29:23] Ray's user interface vulnerabilities lead to attacks [32:07] ML Flow vulnerabilities present significant server risks [36:04] Data ops essential for machine learning security [37:32] Prioritized security in model and data deployment [40:46] Automated scanning tool for improved antivirus protection [42:00] Wrap up…
 
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MLOps for GenAI Applications // Harcharan Kabbay // #256 1:07:18

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Harcharan Kabbay is a Data Scientist & AI/ML Engineer with Expertise in MLOps, Kubernetes, and DevOps, Driving End-to-End Automation and Transforming Data into Actionable Insights. MLOps for GenAI Applications // MLOps Podcast #256 with Harcharan Kabbay, Lead Machine Learning Engineer at World Wide Technology. // Abstract The discussion begins with a brief overview of the Retrieval-Augmented Generation (RAG) framework, highlighting its significance in enhancing AI capabilities by combining retrieval mechanisms with generative models. The podcast further explores the integration of MLOps, focusing on best practices for embedding the RAG framework into a CI/CD pipeline. This includes ensuring robust monitoring, effective version control, and automated deployment processes that maintain the agility and efficiency of AI applications. A significant portion of the conversation is dedicated to the importance of automation in platform provisioning, emphasizing tools like Terraform. The discussion extends to application design, covering essential elements such as key vaults, configurations, and strategies for seamless promotion across different environments (development, testing, and production). We'll also address how to enhance the security posture of applications through network firewalls, key rotation, and other measures. Let's talk about the power of Kubernetes and related tools to aid a good application design. The podcast highlights the principles of good application design, including proper observability and eliminating single points of failure. I would share strategies to reduce development time by creating templates for GitHub repositories by application types to be reused, also templates for pull requests, thereby minimizing human errors and streamlining the development process. // Bio Harcharan is an AI and machine learning expert with a robust background in Kubernetes, DevOps, and automation. He specializes in MLOps, facilitating the adoption of industry best practices and platform provisioning automation. With extensive experience in developing and optimizing ML and data engineering pipelines, Harcharan excels at integrating RAG-based applications into production environments. His expertise in building scalable, automated AI systems has empowered the organization to enhance decision-making and problem-solving capabilities through advanced machine-learning techniques. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Harcharan's Medium - https://medium.com/@harcharan-kabbay Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Harcharan on LinkedIn: https://www.linkedin.com/in/harcharankabbay/locale=en_US Timestamps: [00:00] Harcharan's preferred coffee [00:21] Takeaways [01:03] Against local LLMs [02:11] Creating bad habits [02:42] Operationalizing RAG from CICD perspective [09:39] Kubernetes vs LLM Deployment [12:12] Tool preferences in ML [14:39] DevOps perspective of deployment [17:44] Terraform Licensing Controversy [22:47] PR Review Template Guidance [27:32] People process tech order [29:22] Register for the Data Engineering for AI/ML Conference now! [30:00] ML monitoring strategies explained [39:39] Serverless vs Overprovisioning [44:43] Model SLA's and Monitoring [51:04] LLM to App transition [52:42] Ensuring Robust Architecture [58:53] Chaos engineering in ML [1:04:43] Wrap up…
 
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BigQuery Feature Store // Nicolas Mauti // #255 50:38

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Nicolas Mauti is an MLOps Engineer from Lyon (France), Working at Malt . BigQuery Feature Store // MLOps Podcast #255 with Nicolas Mauti, Lead MLOps at Malt. // Abstract Need a feature store for your AI/ML applications but overwhelmed by the multitude of options? Think again. In this talk, Nicolas shares how they solved this issue at Malt by leveraging the tools they already had in place. From ingestion to training, Nicolas provides insights on how to transform BigQuery into an effective feature management system. We cover how Nicolas' team designed their feature tables and addressed challenges such as monitoring, alerting, data quality, point-in-time lookups, and backfilling. If you’re looking for a simpler way to manage your features without the overhead of additional software, this talk is for you. Discover how BigQuery can handle it all! // Bio Nicolas Mauti is the go-to guy for all things related to MLOps at Malt. With a knack for turning complex problems into streamlined solutions and over a decade of experience in code, data, and ops, he is a driving force in developing and deploying machine learning models that actually work in production. When he's not busy optimizing AI workflows, you can find him sharing his knowledge at the university. Whether it's cracking a tough data challenge or cracking a joke, Nicolas knows how to keep things interesting. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related LinksNicolas' Medium - https://medium.com/@nmauti Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Nicolas on LinkedIn: https://www.linkedin.com/in/nicolasmauti/?locale=en_US Timestamps: [00:00] Nicolas' preferred beverage [00:35] Takeaways [02:25] Please like, share, leave a review, and subscribe to our MLOps channels! [02:57] BigQuery end goal [05:00] BigQuery pain points [10:14] BigQuery vs Feature Stores [12:54] Freelancing Rate Matching issues [16:43] Post-implementation pain points [19:39] Feature Request Process [20:45] Feature Naming Consistency [23:42] Feature Usage Analysis [26:59] Anomaly detection in data [28:25] Continuous Model Retraining Process [30:26] Model misbehavior detection [33:01] Handling model latency issues [36:28] Accuracy vs The Business [38:59] BigQuery cist-benefit analysis [42:06] Feature stores cost savings [44:09] When not to use BigQuery [46:20] Real-time vs Batch Processing [49:11] Register for the Data Engineering for AI/ML Conference now! [50:14] Wrap up…
 
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Design and Development Principles for LLMOps // Andy McMahon // #254 1:10:17

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Design and Development Principles for LLMOps // MLOps Podcast #254 with Andy McMahon, Director - Principal AI Engineer at Barclays Bank. A huge thank you to SAS for their generous support! // Abstract As we move from MLOps to LLMOps we need to double down on some fundamental software engineering practices, as well as augment and add to these with some new techniques. In this case, let's talk about this! // Bio Andy is a Principal AI Engineer, working in the new AI Center of Excellence at Barclays Bank. Previously, he was Head of MLOps for NatWest Group, where he led their MLOps Centre of Excellence and helped build out their MLOps platform and processes across the bank. Andy is also the author of Machine Learning Engineering with Python, a hands-on technical book published by Packt. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related LinksAndy's book - https://packt.link/w3JKL Andy's Medium - https://medium.com/@andrewpmcmahon629 SAS: https://www.sas.com/en_us/home.html SAS® Decision Builder: https://www.sas.com/en_us/offers/23q4/microsoft-fabric.html Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai Harnessing MLOps in Finance // Michelle Marie Conway // MLOps Podcast Coffee #174: https://youtu.be/nIEld_Q6L-0 The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses book by Eric Ries: https://www.amazon.co.jp/-/en/Eric-Ries/dp/0307887898 --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Andy on LinkedIn: https://www.linkedin.com/in/andrew-p-mcmahon/ Timestamps: [00:00] Andy's preferred coffee [00:09] Takeaways [02:04] Andy's book as an Oxford curriculum [06:13] Register for the Data Engineering for AI/ML Conference now! [07:04] The Life Cycle of AI Executives Course [09:55] MLOps as a term [11:53] Tooling vs Process Culture [15:01] Open source benefits [17:15] End goal flexibility [20:06] Hybrid Cloud Strategy Overview [21:11] ROI for tool upgrades [25:41] Long-term projects comparison [29:02 - 30:48] SAS Ad [30:49] AI and ML Integration [35:40] Hybrid AI Integration Insights [42:18] Tech trends vs Practicality [44:39] Gen AI Tooling Debate [51:57] Vanity metrics overview [55:22] Tech business alignment strategy [58:45] Aligning teams for ROI [1:01:35] Communication mission effectively [1:03:45] Enablement metrics [1:06:38] Prioritizing use cases [1:09:47] Wrap up…
 
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Data Quality = Quality AI // AIQCON Panel 27:14

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// Abstract Data is the foundation of AI. To ensure AI performs as expected, high-quality data is essential. In this panel discussion, Chad, Maria, Joe, and Pushkar hosted by Sam Partee will explore strategies for obtaining and maintaining high-quality data, as well as common pitfalls to avoid when using data for AI models. // Panelists - Samuel Partee: Principal Applied AI Engineer @ Redis - Chad Sanderson: CEO & Co-Founder @ Gable - Joe Reis: CEO/Co-Founder @ Ternary Data - Maria Zhang: CEO Cofounder @ Proactive AI Lab Inc - Pushkar Garg: Staff Machine Learning Engineer @ Clari Inc.…
 
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The Variational Book // Yuri Plotkin // #253 55:35

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Yuri Plotkin is a Biomedical Engineer and Machine Learning Scientist, and the author of The Variational Book . The Variational Book // MLOps Podcast #253 with Yuri Plotkin, an ML Scientist. // Abstract Curiosity has been the underlying thread in Yuri's life and interests. With the explosion of Generative AI, Yuri was fascinated by the topic and decided he needed to learn more. Yuri pursued learning by reading, deriving, and understanding seminal papers within the last generation. The endeavors culminated in the writing of a book on the topic, The Variational Book, which Yuri expects to release shortly in the coming months. A bit of detail about the topics he covers can be found here: www.thevariationalbook.com . // Bio Evolved from biomedical engineer to wet-lab scientist, and more recently transitioned Yuri's career to computer science with the last 10+ years developing projects at the intersection of medicine, life sciences, and machine learning. Yuri's educational background is in Biomedical Engineering, at Columbia University (M.S.) and the University of California, San Diego (B.S.). Current interests include generative AI, diffusion models, and LLMs. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://plotkiny.github.io/ The Variational Book: www.thevariationalbook.com SAS: https://www.sas.com/en_us/home.html SAS® Decision Builder: https://www.sas.com/en_us/offers/23q4/microsoft-fabric.html Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Yuri on LinkedIn: http://www.linkedin.com/in/yuri-plotkin/ Timestamps: [00:00] Yuri's preferred coffee [00:23] Takeaways [01:56] Register for the Data Engineering for AIML Conference now! [02:47] Yuri's background [06:13] The Variational Book [10:25] Not including LLMs in the book [12:14] Diffusion models [16:37] Evolution within diffusion models [20:55] Diffusion models for video [25:43] Evolution and optimization of algorithms [28:53] Markovian [33:06 - 34:52] SAS Ad [34:53] Understanding Markovian vs Non-Markovian models [40:28] Visualizing model evolution [43:46] Models through time [44:53] The Variational Book inspiration [47:53] Influencing LLM latent space [51:07] Understanding ML Architectures [52:56] Balancing AI complexity [55:05] Wrap up…
 
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Vision and Strategies for Attracting & Driving AI Talents in High Growth // Panel // AIQCON 30:33

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// Abstract Attracting and retaining top AI talent is essential for staying competitive. This panel will explore crafting and communicating a compelling vision that aligns with the organization's evolving needs, inspiring potential hires and motivating current employees. The discussion will offer actionable strategies for sourcing top talent, adapting to changing needs, and maintaining company alignment. Attendees will learn best practices for attracting AI professionals, creating an attractive employer brand, and enhancing talent acquisition and retention strategies. Lastly, the panel will cover structuring and organizing the AI team as it grows to ensure alignment with business goals. This includes optimal team configurations, leadership roles, and processes that support collaboration and innovation, enabling sustained growth and success. // PANELISTS Ashley Antonides : Associate Research Director, AI/ML @ Two Six Technologies Olga Beregovaya : VP, AI @ Smartling Shailvi Wakhlu : Founder @ Shailvi Ventures LLC A big thank you to our Premium Sponsors Google Cloud & Databricks for their generous support!…
 
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Red Teaming LLMs // Ron Heichman // #252 1:09:52

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Ron Heichmn is an AI researcher specializing in generative AI, AI alignment, and prompt engineering. At SentinelOn e , Ron actively monitors emerging research to identify and address potential vulnerabilities in our AI systems, focusing on unsupervised and scalable evaluations to ensure robustness and reliability. Harnessing AI APIs for Safer, Accurate, & Reliable Applications // MLOps Podcast #252 with Ron Heichman, Machine Learning Engineer at SentinelOne. // Abstract Integrating AI APIs effectively is pivotal for building applications that leverage LLMs, especially given the inherent issues with accuracy, reliability, and safety that LLMs often exhibit. I aim to share practical strategies and experiences for using AI APIs in production settings, detailing how to adapt these APIs to specific use cases, mitigate potential risks, and enhance performance. The focus will be testing, measuring, and improving quality for RAG or knowledge workers utilizing AI APIs. // Bio Ron Heichman is an AI researcher and engineer dedicated to advancing the field through his work on prompt injection at Preamble, where he helped uncover critical vulnerabilities in AI systems. Currently at SentinelOne, he specializes in generative AI, AI alignment, and the benchmarking and measurement of AI system performance, focusing on Retrieval-Augmented Generation (RAG) and AI guardrails. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.sentinelone.com/ All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://www.youtube.com/watch?v=DZgXln3v85s&ab_channel=MLOps.community --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ron on LinkedIn: https://www.linkedin.com/in/heichmanron/ Timestamps: [00:00] Ron's preferred coffee [00:20] Takeaways [01:08] Register now for the Data Engineering for AIML Conference! [01:59] AI vs ML Solutions [05:42] AI Application challenges [09:38] AI Model evolution [19:22] AI tools accessibility challenge [20:53] AI tools accessibility gap [24:00] Optimizing LLM Performance [30:31] Red teaming taxonomy [36:11] Securing custom LLMs [44:32] Diverse data in LLMs [46:29] Automated data diversity feedback [50:42] Model stress-testing process [55:49] Early issue detection benefits [57:41] Prompt injection patterns [1:02:11] Best jailbreaks seen by Ron [1:04:53] Data poisoning vulnerabilities [1:07:48] Wrap up…
 
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Balancing Speed and Safety // Panel // AIQCON 35:39

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This is a panel taken from the recent AI quality Conference presented by the MLOps Community and Kolena // Abstract The need for moving to production quickly is paramount in staying out of perpetual POC territory. AI is moving fast. Shipping features fast to stay ahead of the competition is commonplace. Quick iterations are viewed as strength in the startup ecosystem, especially when taking on a deeply entrenched competitor. Each week a new method to improve your AI system becomes popular or a SOTA foundation model is released. How do we balance the need for speed vs the responsibility of safety? Having the confidence to ship a cutting-edge model or AI architecture and knowing it will perform as tasked. What are the risks and safety metrics that others are using when they deploy their AI systems. How can you correctly identify when risks are too large? // Panelists - Remy Thellier: Head of Growth & Strategic Partnerships @ Vectice - Erica Greene: Director of Engineering, Machine Learning @ Yahoo - Shreya Rajpal: Creator @ Guardrails AI A big thank you to our Premium Sponsors Google Cloud & Databricks for their generous support!…
 
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Reliable LLM Products, Fueled by Feedback // Chinar Movsisyan // #251 49:16

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Chinar Movsisyan is the co-founder and CEO of Feedback Intelligence (formerly Manot) , an MLOps startup based in San Francisco. She has been in the AI field for more than 7 years, from research labs to venture-backed startups. Reliable LLM Products, Fueled by Feedback // MLOps Podcast #250 with Chinar Movsisyan, CEO of Feedback Intelligence. // Abstract We live in a world driven by large language models (LLMs) and generative AI, but ensuring they are ready for real-world deployment is crucial. Despite the availability of numerous evaluation tools, many LLM products still struggle to make it to production. We propose a new perspective on how LLM products should be measured, evaluated, and improved. A product is only as good as the user's experience and expectations, and we aim to enhance LLM products to meet these standards reliably. Our approach creates a new category that automates the need for separate evaluation, observability, monitoring, and experimentation tools. By starting with the user experience and working backward to the model, we provide a comprehensive view of how the product is actually used, rather than how it is intended to be used. This user-centric, aka feedback-centric, approach is the key to every successful product. // Bio Chinar Movsisyan is the founder and CEO of Feedback Intelligence, an MLOps company based in San Francisco that enables enterprises to make sure that LLM-based products are reliable and that the output is aligned with end-user expectations. With over eight years of experience in deep learning, spanning from research labs to venture-backed startups, Chinar has led AI projects in mission-critical applications such as healthcare, drones, and satellites. Her primary research interests include artificial intelligence, generative AI, machine learning, deep learning, and computer vision. At Feedback Intelligence, Chinar and her team address a crucial challenge in LLM development by automatically converting user feedback into actionable insights, enabling AI teams to analyze root causes, prioritize issues, and accelerate product optimization. This approach is particularly valuable in highly regulated industries, helping enterprises to reduce time-to-market and time-to-resolution while ensuring robust LLM products. Feedback Intelligence, which participated in the Berkeley SkyDeck accelerator program, is currently expanding its business across various verticals. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.manot.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Chinar on LinkedIn: https://www.linkedin.com/in/nik-suresh/ Timestamps: [00:00] Chinar's preferred coffee [00:20] Takeaways [02:25] Please like, share, leave a review, and subscribe to our MLOps channels! [03:23] Object Detection on Drones [06:10] Street Surveillance Detection Use Case [08:00] Optimizing Vision Models [09:50] Data Engineering for AI/ML Conference Ad [10:42] Plastic surgery project [12:33] Diffusion models getting popular [13:57] AI challenges in highly regulated industries [17:48] Product metrics evaluation insights [20:55] Chatbot effectiveness metrics [23:15] Interpreting user signals [24:45] Metadata tracking in LLM [27:41] Agentic workflow [28:53] Effective data analysis strategies [30:41] Identifying key metrics [33:59] AI metrics role shift [37:20] Tooling for non-engineers [42:12] Balancing engineering and evaluation [44:39] Bridging SME engineering gap [46:41] Expand expertise potential [47:40] What's with flamingos [48:04] Wrap up…
 
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A Blueprint for Scalable & Reliable Enterprise AI/ML Systems // Panel // AIQCON 35:38

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This is a Panel taken from the recent AI Quality Conference presented by the MLOps COmmunity and Kolena // Abstract Enterprise AI leaders continue to explore the best productivity solutions that solve business problems, mitigate risks, and increase efficiency. Building reliable and secure AI/ML systems requires following industry standards, an operating framework, and best practices that can accelerate and streamline the scalable architecture that can produce expected business outcomes. This session, featuring veteran practitioners, focuses on building scalable, reliable, and quality AI and ML systems for the enterprises. // Panelists - Hira Dangol: VP, AI/ML and Automation @ Bank of America - Rama Akkiraju: VP, Enterprise AI/ML @ NVIDIA - Nitin Aggarwal: Head of AI Services @ Google - Steven Eliuk: VP, AI and Governance @ IBM A big thank you to our Premium Sponsors Google Cloud & Databricks for their generous support! Timestamps: 00:00 Panelists discuss vision and strategy in AI 05:18 Steven Eliuk, IBM expertise in data services 07:30 AI as means to improve business metrics 11:10 Key metrics in production systems: efficiency and revenue 13:50 Consistency in data standards aids data integration 17:47 Generative AI presents new data classification risks 22:47 Evaluating implications, monitoring, and validating use cases 26:41 Evaluating natural language answers for efficient production 29:10 Monitoring AI models for performance and ethics 31:14 AI metrics and user responsibility for future models 34:56 Access to data is improving, promising progress…
 
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AI Operations Without Fundamental Engineering Discipline // Nikhil Suresh // #250 49:28

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Nik Suresh wrote an evisceration of the current AI hype boom called "I Will F**king Piledrive You If You Mention AI Again." AI Operations Without Fundamental Engineering Discipline // MLOps Podcast #250 with Nikhil Suresh, Director @ Hermit Tech. // Abstract Nik is on the podcast because of an anti-AI hype piece, so a reasonable thing to discuss is going to be what most companies are getting wrong when non-technical management wants to immediately roll out ML initiatives, but are unwilling to bring technical naysayers on board who will set them up for success. // Bio Nik is the author of ludic.mataroa.blog, who wrote "I Will [REDACTED] Piledriver You If You Mention AI Again", and mostly works in the data engineering and data science spaces. Nik's writing and company both focus on bringing more care to work, pushing back against the industry's worst excesses both technically and spiritually, and getting fundamentals right. Nik also has a reasonably strong background in psychology. His data science training was of the pre-LLM variety, circa. 2018, when there was a lot of hype but it wasn't this ridiculous. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://ludic.mataroa.blog/ Nik's blog: https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/ Harnessing MLOps in Finance // Michelle Marie Conway // MLOps Podcast Coffee #174: https://youtu.be/nIEld_Q6L-0 Fundamentals of Data Engineering: Plan and Build Robust Data Systems AudiobookBy: Joe Reis, Matt Housley: https://audiobookstore.com/audiobooks/fundamentals-of-data-engineering.aspx Bullshit Jobs A Theory Hardcover by David Graeber : https://www.amazon.co.jp/-/en/David-Graeber/dp/0241263883 Does a Frog have Scorpion Nature podcast: https://open.spotify.com/show/57i8sYVqxG4i3NvBniLfhv --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Nik on LinkedIn: https://www.linkedin.com/in/nik-suresh/ Timestamps: [00:00] Nik's preferred coffee [00:30] Takeaways [01:40] Please like, share, leave a review, and subscribe to our MLOps channels! [01:56] AI hype and humor [07:21] Defining project success [08:57] Effective data utilization [12:18] AI Hype vs Data Engineering [14:44] AI implementation challenges [17:44 - 18:35] Data Engineering for AI and ML Virtual Conference Ad [18:35] Managing AI Expectations [22:08] AI expectations vs reality [26:00] Balancing Engineering and AI [31:54] Highlighting engineer success [35:25] The real challenges [36:30] Embracing work challenges [37:21] Dealing with podcast disappointments [40:50] Creating content for visibility [43:02] Exploring niche interests [44:14] Relationship building [47:15] Strategic approach to success [48:36] Wrap up…
 
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AI in Healthcare // Eric Landry // #249 51:05

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Eric Landry is a seasoned AI and Machine Learning leader with extensive expertise in software engineering and practical applications in NLP, document classification, and conversational AI. With technical proficiency in Java, Python, and key ML tools, he leads the Expedia Machine Learning Engineering Guild and has spoken at major conferences like Applied Intelligence 2023 and KDD 2020. AI in Healthcare // MLOps Podcast #249 with Eric Landry, CTO/CAIO @ Zeteo Health. // Abstract Eric Landry discusses the integration of AI in healthcare, highlighting use cases like patient engagement through chatbots and managing medical data. He addresses benchmarking and limiting hallucinations in LLMs, emphasizing privacy concerns and data localization. Landry maintains a hands-on approach to developing AI solutions and navigating the complexities of healthcare innovation. Despite necessary constraints, he underscores the potential for AI to proactively engage patients and improve health outcomes. // Bio Eric Landry is a technology veteran with 25+ years of experience in the healthcare, travel, and computer industries, specializing in machine learning engineering and AI-based solutions. Holding a Masters in SWE (NLP thesis topic) from the University of Texas at Austin, 2005. He has showcased his expertise and leadership in the field with three US patents, published articles on machine learning engineering, and speaking engagements at the 2023 Applied Intelligence Live, 2020 KDD conference, Data Science Salon 2024, and former leader of Expedia’s MLE guild. Formerly, Eric was the director of AI Engineering and Conversation Platform at Babylon Health and Expedia. Currently CTO/CAIO at Zeteo Health. // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.zeteo.health/ Building Threat Detection Systems: An MLE's Perspective // Jeremy Jordan // MLOps Podcast #134: https://youtu.be/13nOmMJuiAo --------------- ✌️Connect With Us ✌️ ------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Eric on LinkedIn: https://www.linkedin.com/in/jeric-landry/ Timestamps: [00:00] Eric's preferred coffee [00:16] Takeaways [01:16] Please like, share, leave a review, and subscribe to our MLOps channels! [01:32] ML and AI in 2005 [04:43] Last job at Babylon Health [10:57] Data access solutions [14:35] Prioritize AI ML Team Success [16:39] Eric's current work [20:36] Engage in holistic help [22:13] High-stakes chatbots [27:30] Navigating Communication Across Diverse Communities [31:49] When Bots Go Wrong [34:15] Health care challenges ahead [36:05] Behavioral health tech challenges [39:45] Stress from Apps Notifications [41:11] Combining different guardrails tools [47:16] Navigating Privacy AI [50:12] Wrap up…
 
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Evaluating the Effectiveness of Large Language Models: Challenges and Insights // Aniket Singh // #248 35:40

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Aniket Kumar Singh is a Vision Systems Engineer at Ultium Cells, skilled in Machine Learning and Deep Learning. I'm also engaged in AI research, focusing on Large Language Models (LLMs). Evaluating the Effectiveness of Large Language Models: Challenges and Insights // MLOps Podcast #248 with Aniket Kumar Singh, CTO @ MyEvaluationPal | ML Engineer @ Ultium Cells. // Abstract Dive into the world of Large Language Models (LLMs) like GPT-4. Why is it crucial to evaluate these models, how we measure their performance, and the common hurdles we face? Drawing from Aniket's research, he shares insights on the importance of prompt engineering and model selection. Aniket also discusses real-world applications in healthcare, economics, and education, and highlights future directions for improving LLMs. // Bio Aniket is a Vision Systems Engineer at Ultium Cells, skilled in Machine Learning and Deep Learning. I'm also engaged in AI research, focusing on Large Language Models (LLMs). // MLOps Jobs board jobs.mlops.community // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.aniketsingh.me Aniket's AI Research for Good blog that I plan to utilize to share any new research that would focus on the good: www.airesearchforgood.org Aniket's papers: https://scholar.google.com/citations?user=XHxdWUMAAAAJ&hl=en --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Aniket on LinkedIn: https://www.linkedin.com/in/singh-k-aniket/ Timestamps: [00:00] Aniket's preferred coffee [00:14] Takeaways [01:29] Aniket's job and hobby [03:06] Evaluating LLMs: Systems-Level Perspective [05:55] Rule-based system [08:32] Evaluation Focus: Model Capabilities [13:04] LLM Confidence [13:56] Problems with LLM Ratings [17:17] Understanding AI Confidence Trends [18:28] Aniket's papers [20:40] Testing AI Awareness [24:36] Agent Architectures Overview [27:05] Leveraging LLMs for tasks [29:53] Closed systems in Decision-Making [31:28] Navigating model Agnosticism [33:47] Robust Pipeline vs Robust Prompt [34:40] Wrap up…
 
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