Towards Observability for ML Pipelines // Shreya Shankar // MLOps Coffee Sessions #75

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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

Lost Cultures: Living Legacies

1
Bermuda: The Crossroads of the Atlantic 51:57

24 روز پیش51:57

پخش در آینده

لیست ها

پسندیدن

دوست داشته شد

51:57

On the Season 2 debut of Lost Cultures: Living Legacies , we travel to Bermuda, an Atlantic island whose history spans centuries and continents. Once uninhabited, Bermuda became a vital stop in transatlantic trade, a maritime stronghold, and a cultural crossroads shaped by African, European, Caribbean, and Native American influences. Guests Dr. Kristy Warren and Dr. Edward Harris trace its transformation from an uninhabited island to a strategic outpost shaped by shipwrecks, colonization, the transatlantic slave trade, and the rise and fall of empires. Plus, former Director of Tourism Gary Phillips shares the story of the Gombey tradition, a vibrant performance art rooted in resistance, migration, and cultural fusion. Together, they reveal how Bermuda’s layered past continues to shape its people, culture, and identity today. You can also find us online at ⁠travelandleisure.com/lostcultures Learn more about your ad choices. Visit podcastchoices.com/adchoices…

۳ سال پیش 57:09

M4A•خانه قسمت

MLOps Coffee Sessions #75 with Shreya Shankar, Towards Observability for ML Pipelines.
// Abstract
Achieving observability in ML pipelines is a mess right now. We are tracking thousands of means, percentiles, and KL divergences of features and outputs in a haphazard attempt to figure out when and how to retrain models.
In this session, we break down current unsuccessful approaches and discuss the path towards effectively maintaining ML models in production. Along the way, we introduce mltrace -- a preliminary open source project striving towards "bolt-on" observability in ML pipelines.
// Bio
Shreya Shankar is a computer scientist living in the Bay Area. She's interested in building systems to operationalize machine learning workflows. Shreya's research focus is on end-to-end observability for ML systems, particularly in the context of heterogeneous stacks of tools.
Currently, Shreya is doing her Ph.D. in the RISE lab at UC Berkeley. Previously, she was the first ML engineer at Viaduct, did research at Google Brain, and completed her BS and MS in computer science at Stanford University.
// Related Links
Shreya Shankar's blogposts: https://www.shreya-shankar.com/
Shreya Shankar's Podcasts: https://www.listennotes.com/top-episodes/shreya-shankar/
The deployment phase of machine learning by Benedict Evans: https://www.ben-evans.com/benedictevans/2019/10/4/machine-learning-deployment
Shreya Shrankar's mltrace blogpost: https://www.shreya-shankar.com/introducing-mltrace/
--------------- ✌️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 Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Shreya on LinkedIn: https://www.linkedin.com/in/shrshnk
Timestamps:
[00:00] Introduction to Shreya Shankar
[01:12] Shreya's background
[03:22] Contrast in scale influence
[05:28] Embedding ML and building machine learning infused products
[07:26] Management structure and professional incentive
[08:25] Organizational side of MLOps retros
[10:15] Tooling implementations
[12:00] Structured rational investment hardships
[13:17] Working at a start-up
[14:02] Academic work and entrepreneurial ambitions
[16:00] ML Monitoring Observability interest
[17:14] Where to get started
[20:47] Realization while at Viaduct
[23:30] Preventing alert fatigue
[27:04] Tooling bridging the gap
[30:40] Juncture at overall MLOps ecosystem
[33:58] The deployment phase of machine learning - it's the new SQL by Benedict Evans
[35:30] Model monitoring
[36:16] mltrace
[38:28] Introducing mltrace blog post series
[41:25] Tips to our content creators/writers
[43:47] Monitoring through the lens of the database
[47:37] Advice about picking up ML engineering and ML systems development in 2022
[49:36] Database low down the stack
[50:51] Most excited about 2022
[52:13] What MLOps space/ecosystem should change?
[53:21] Funding has changed the incentives around innovation
[54:52] Competition in million-dollar rounds
[55:25] Starting a company
[56:30] Wrap up

443 قسمت

Towards Observability for ML Pipelines // Shreya Shankar // MLOps Coffee Sessions #75

MLOps.community

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published ۳ سال پیش

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M4A•خانه قسمت

443 قسمت

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1
Everything Hard About Building AI Agents Today 47:02

15 hours پیش47:02

47:02

Willem Pienaar and Shreya Shankar discuss the challenge of evaluating agents in production where "ground truth" is ambiguous and subjective user feedback isn't enough to improve performance. The discussion breaks down the three "gulfs" of human-AI interaction—Specification, Generalization, and Comprehension—and their impact on agent success. Willem and Shreya cover the necessity of moving the human "out of the loop" for feedback, creating faster learning cycles through implicit signals rather than direct, manual review.The conversation details practical evaluation techniques, including analyzing task failures with heat maps and the trade-offs of using simulated environments for testing. Willem and Shreya address the reality of a "performance ceiling" for AI and the importance of categorizing problems your agent can, can learn to, or will likely never be able to solve. // Bio Shreya Shankar PhD student in data management for machine learning. Willem Pienaar 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 https://www.google.com/about/careers/applications/?utm_campaign=profilepage&utm_medium=profilepage&utm_source=linkedin&src=Online/LinkedIn/linkedin_pagehttps://cleric.ai/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Shreya on LinkedIn: /shrshnk Connect with Willem on LinkedIn: /willempienaar Timestamps: [00:00] Trust Issues in AI Data [04:49] Cloud Clarity Meets Retrieval [09:37] Why Fast AI Is Hard [11:10] Fixing AI Communication Gaps [14:53] Smarter Feedback for Prompts [19:23] Creativity Through Data Exploration [23:46] Helping Engineers Solve Faster [26:03] The Three Gaps in AI [28:08] Alerts Without the Noise [33:22] Custom vs General AI [34:14] Sharpening Agent Skills [40:01] Catching Repeat Failures [43:38] Rise of Self-Healing Software [44:12] The Chaos of Monitoring AI…

1
Tricks to Fine Tuning // Prithviraj Ammanabrolu // #318 54:01

۳ روز پیش54:01

54:01

Tricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu, Research Scientist at Databricks . Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Prithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment. // Bio Raj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems. Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab. // Related Links Website: https://www.databricks.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 Raj on LinkedIn: /rajammanabrolu Timestamps: [00:00] Raj's preferred coffee [00:36] Takeaways [01:02] Tao Naming Decision [04:19] No Labels Machine Learning [08:09] Tao and TAO breakdown [13:20] Reward Model Fine-Tuning [18:15] Training vs Inference Compute [22:32] Retraining and Model Drift [29:06] Prompt Tuning vs Fine-Tuning [34:32] Small Model Optimization Strategies [37:10] Small Model Potential [43:08] Fine-tuning Model Differences [46:02] Mistral Model Freedom [53:46] Wrap up…

1
Packaging MLOps Tech Neatly for Engineers and Non-engineers // Jukka Remes // #322 55:30

۴ روز پیش55:30

55:30

Packaging MLOps Tech Neatly for Engineers and Non-engineers // MLOps Podcast #322 with Jukka Remes, Senior Lecturer (SW dev & AI), AI Architect at Haaga-Helia UAS, Founder & CTO at 8wave AI. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract AI is already complex—adding the need for deep engineering expertise to use MLOps tools only makes it harder, especially for SMEs and research teams with limited resources. Yet, good MLOps is essential for managing experiments, sharing GPU compute, tracking models, and meeting AI regulations. While cloud providers offer MLOps tools, many organizations need flexible, open-source setups that work anywhere—from laptops to supercomputers. Shared setups can boost collaboration, productivity, and compute efficiency.In this session, Jukka introduces an open-source MLOps platform from Silo AI, now packaged for easy deployment across environments. With Git-based workflows and CI/CD automation, users can focus on building models while the platform handles the MLOps.// BioFounder & CTO, 8wave AI | Senior Lecturer, Haaga-Helia University of Applied SciencesJukka Remes has 28+ years of experience in software, machine learning, and infrastructure. Starting with SW dev in the late 1990s and analytics pipelines of fMRI research in early 2000s, he’s worked across deep learning (Nokia Technologies), GPU and cloud infrastructure (IBM), and AI consulting (Silo AI), where he also led MLOps platform development. Now a senior lecturer at Haaga-Helia, Jukka continues evolving that open-source MLOps platform with partners like the University of Helsinki. He leads R&D on GenAI and AI-enabled software, and is the founder of 8wave AI, which develops AI Business Operations software for next-gen AI enablement, including regulatory compliance of AI. // Related Links Open source -based MLOps k8s platform setup originally developed by Jukka's team at Silo AI - free for any use and installable in any environment from laptops to supercomputing: https://github.com/OSS-MLOPS-PLATFORM/oss-mlops-platform Jukka's new company:https://8wave.ai ~~~~~~~~ ✌️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 Jukka on LinkedIn: /jukka-remes Timestamps: [00:00] Jukka's preferred coffee [00:39] Open-Source Platform Benefits [01:56] Silo MLOps Platform Explanation [05:18] AI Model Production Processes [10:42] AI Platform Use Cases [16:54] Reproducibility in Research Models [26:51] Pipeline setup automation [33:26] MLOps Adoption Journey [38:31] EU AI Act and Open Source [41:38] MLOps and 8wave AI [45:46] Optimizing Cross-Stakeholder Collaboration [52:15] Open Source ML Platform [55:06] Wrap up…

1
Hard Learned Lessons from Over a Decade in AI 48:42

۸ روز پیش48:42

48:42

Tecton⁠ Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack. // Bio Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto. // Related Links Website: www.tecton.ai ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Mike on LinkedIn: /michaeldelbalso Timestamps: [00:00] Smarter decisions, less manual work [03:52] Data pipelines: pain and fixes [08:45] Why Tecton was born [11:30] ML use cases shift [14:14] Models for big bets [18:39] Build or buy drama [20:20] Fintech's data playbook [23:52] What really needs real-time [28:07] Speeding up ML delivery [32:09] Valuing ML is tricky [35:29] Simplifying ML toolkits [37:18] AI copilots in action [42:13] AI that fights fraud [45:07] Teaming up across coasts [46:43] Tecton + Generative AI?…

1
Product Metrics are LLM Evals // Raza Habib CEO of Humanloop // #320 53:06

11 روز پیش53:06

53:06

Raza Habib, the CEO of LLM Eval platform Humanloop , talks to us about how to make your AI products more accurate and reliable by shortening the feedback loop of your evals. Quickly iterating on prompts and testing what works, along with some of his favorite Dario from Anthropic AI Quotes. // Bio Raza is the CEO and Co-founder at Humanloop. He has a PhD in Machine Learning from UCL, was the founding engineer of Monolith AI, and has built speech systems at Google. For the last 4 years, he has led Humanloop and supported leading technology companies such as Duolingo, Vanta, and Gusto to build products with large language models. Raza was featured in the Forbes 30 Under 30 technology list in 2022, and Sifted recently named him one of the most influential Gen AI founders in Europe. // Related Links Websites: https://humanloop.com ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Raza on LinkedIn: /humanloop-raza Timestamps: [00:00] Cracking Open System Failures and How We Fix Them [05:44] LLMs in the Wild — First Steps and Growing Pains [08:28] Building the Backbone of Tracing and Observability [13:02] Tuning the Dials for Peak Model Performance [13:51] From Growing Pains to Glowing Gains in AI Systems [17:26] Where Prompts Meet Psychology and Code [22:40] Why Data Experts Deserve a Seat at the Table [24:59] Humanloop and the Art of Configuration Taming [28:23] What Actually Matters in Customer-Facing AI [33:43] Starting Fresh with Private Models That Deliver [34:58] How LLM Agents Are Changing the Way We Talk [39:23] The Secret Lives of Prompts Inside Frameworks [42:58] Streaming Showdowns — Creativity vs. Convenience [46:26] Meet Our Auto-Tuning AI Prototype [49:25] Building the Blueprint for Smarter AI [51:24] Feedback Isn’t Optional — It’s Everything…

1
Getting AI Apps Past the Demo // Vaibhav Gupta // #319 50:29

15 روز پیش50:29

50:29

Getting AI Apps Past the Demo // MLOps Podcast #319 with Vaibhav Gupta, CEO of BoundaryML. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract It's been two years, and we still seem to see AI disproportionately more in demos than production features. Why? And how can we apply engineering practices we've all learned in the past decades to our advantage here? // Bio Vaibhav is one of the creators of BAML and a YC alum. He spent 10 years in AI performance optimization at places like Google, Microsoft, and D.E. Shaw. He loves diving deep and chatting about anything related to Gen AI and Computer Vision! // Related Links Website: https://www.boundaryml.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 Vaibhav on LinkedIn: /vaigup Timestamps: [00:00] Vaibhav's preferred coffee [00:38] What is BAML [03:07] LangChain Overengineering Issues [06:46] Verifiable English Explained [11:45] Python AI Integration Challenges [15:16] Strings as First-Class Code [21:45] Platform Gap in Development [30:06] Workflow Efficiency Tools [33:10] Surprising BAML Insights [40:43] BAML Cool Projects [45:54] BAML Developer Conversations [48:39] Wrap up…

1
Building Out GPU Clouds // Mohan Atreya // #317 47:57

21 روز پیش47:57

47:57

Demetrios and Mohan Atreya break down the GPU madness behind AI — from supply headaches and sky-high prices to the rise of nimble GPU clouds trying to outsmart the giants. They cover power-hungry hardware, failed experiments, and how new cloud models are shaking things up with smarter provisioning, tokenized access, and a whole lotta hustle. It's a wild ride through the guts of AI infrastructure — fun, fast, and full of sparks! Big thanks to the folks at Rafay for backing this episode — appreciate the support in making these conversations happen! // BioMohan is a seasoned and innovative product leader currently serving as the Chief Product Officer at Rafay Systems. He has led multi-site teams and driven product strategy at companies like Okta, Neustar, and McAfee. // Related LinksWebsites: https://rafay.co/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Mohan on LinkedIn: /mohanatreya Timestamps: [00:00] AI/ML Customer Challenges [04:21] Dependency on Microsoft for Revenue [09:08] Challenges of Hypothesis in AI/ML [12:17] Neo Cloud Onboarding Challenges [15:02] Elastic GPU Cloud Automation [19:11] Dynamic GPU Inventory Management [20:25] Terraform Lacks Inventory Awareness [26:42] Onboarding and End-User Experience Strategies [29:30] Optimizing Storage for Data Efficiency [33:38] Pizza Analogy: User Preferences [35:18] Token-Based GPU Cloud Monetization [39:01] Empowering Citizen Scientists with AI [42:31] Innovative CFO Chatbot Solutions [47:09] Cloud Services Need Spectrum…

1
A Candid Conversation Around MCP and A2A // Rahul Parundekar and Sam Partee // #316 SF Live 1:04:42

24 روز پیش1:04:42

1:04:42

Demetrios, Sam Partee, and Rahul Parundekar unpack the chaos of AI agent tools and the evolving world of MCP (Model Context Protocol). With sharp insights and plenty of laughs, they dig into tool permissions, security quirks, agent memory, and the messy path to making agents actually useful. // Bio Sam Partee Sam Partee is the CTO and Co-Founder of Arcade AI. Previously a Principal Engineer leading the Applied AI team at Redis, Sam led the effort in creating the ecosystem around Redis as a vector database. He is a contributor to multiple OSS projects including Langchain, DeterminedAI, LlamaIndex and Chapel amongst others. While at Cray/HPE he created the SmartSim AI framework which is now used at national labs around the country to integrate HPC simulations like climate models with AI. Rahul Parundekar Rahul Parundekar is the founder of AI Hero. He graduated with a Master's in Computer Science from USC Los Angeles in 2010, and embarked on a career focused on Artificial Intelligence. From 2010-2017, he worked as a Senior Researcher at Toyota ITC working on agent autonomy within vehicles. His journey continued as the Director of Data Science at FigureEight (later acquired by Appen), where he and his team developed an architecture supporting over 36 ML models and managing over a million predictions daily. Since 2021, he has been working on AI Hero, aiming to democratize AI access, while also consulting on LLMOps(Large Language Model Operations), and AI system scalability. Other than his full time role as a founder, he is also passionate about community engagement, and actively organizes MLOps events in SF, and contributes educational content on RAG and LLMOps at learn.mlops.community. // Related Links Websites: arcade.dev aihero.studio~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore MLOps Swag/Merch: [https://shop.mlops.community/] Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Rahul on LinkedIn: /rparundekar Connect with Sam on LinkedIn: /samparteeTimestamps:[00:00] Agents & Tools, Explained (Without Melting Your Brain) [09:51] MVP Servers: Why Everything’s on Fire (and How to Fix It) [13:18] Can We Actually Trust the Protocol? [18:13] KYC, But Make It AI (and Less Painful) [25:25] Web Automation Tests: The Bugs Strike Back [28:18] MCP Dev: What Went Wrong (and What Saved Us) [33:53] Social Login: One Button to Rule Them All [39:33] What Even Is an AI-Native Developer? [42:21] Betting Big on Smarter Models (High Risk, High Reward) [51:40] Harrison’s Bold New Tactic (With Real-Life Magic Tricks) [55:31] Async Task Handoffs: Herding Cats, But Digitally [1:00:37] Getting AI to Actually Help Your Workflow [1:03:53] The Infamous Varma System Error (And How We Dodge It)…

1
AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315 55:32

29 روز پیش55:32

55:32

AI in M&A: Building, Buying, and the Future of Dealmaking // MLOps Podcast #315 with Kison Patel, CEO and M&A Science at DealRoom . Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractThe intersection of M&A and AI, exploring how the DealRoom team developed AI capabilities and the practical use cases of AI in dealmaking. Discuss the evolving landscape of AI-driven M&A, the factors that make AI companies attractive acquisition targets, and the key indicators of success in this space. // Bio Kison Patel is the Founder and CEO of DealRoom, an M&A lifecycle management platform designed for buyer-led M&A and recognized twice on the Inc. 5000 Fastest Growing Companies list. He also founded M&A Science, a global community offering courses, events, and the top-rated M&A Science podcast with over 2.25 million downloads. Through the podcast, Kison shares actionable insights from top M&A experts, helping professionals modernize their approach to deal-making. He is also the author of *Agile M&A: Proven Techniques to Close Deals Faster and Maximize Value*, a guide to tech-enabled, adaptive M&A practices. Kison is dedicated to disrupting traditional M&A with innovative tools and education, empowering teams to drive greater efficiency and value. // Related LinksWebsite: https://dealroom.nethttps://www.mascience.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 Kison on LinkedIn: /kisonpatel…

1
AI, Marketing, and Human Decision Making // Fausto Albers // #313 49:40

۴ weeks پیش49:40

49:40

AI, Marketing, and Human Decision Making // MLOps Podcast #313 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Demetrios and Fausto Albers explore how generative AI transforms creative work, decision-making, and human connection, highlighting both the promise of automation and the risks of losing critical thinking and social nuance. // Bio Fausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you’re stuck. His career has been anything but linear. He’s owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London’s bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems. Ask him about RAG, and he’ll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI. // Related Links Website: aibuilders.club Moravec's paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox?utm_source=chatgpt.com Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311: https://youtu.be/jJXee5rMtHI ~~~~~~~~ ✌️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 Fausto on LinkedIn: /stepintoliquid Timestamps:[00:00] Fausto's preferred coffee[00:26] Takeaways[01:18] Automated Ad Creative Generation[07:14] AI in Marketing Workflows[13:23] MCP and System Bottlenecks[21:45] Forward Compatibility vs Optimization[29:57] Unlocking Workflow Speed[33:48] AI Dependency vs Critical Thinking[37:44] AI Realism and Paradoxes[42:30] Outsourcing Decision-Making Risks[46:22] Human Value in Automation[49:02] Wrap up…

1
MLOps with Databricks // Maria Vechtomova // #314 52:43

۵ weeks پیش52:43

52:43

MLOps with Databricks // MLOps Podcast #314 with Maria Vechtomova, MLOps Tech Lead | Founder at Ahold Delhaize | Marvelous MLOps. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract The world of MLOps is very complex as there is an endless amount of tools serving its purpose, and it is very hard to get your head around it. Instead of combining various tools and managing them, it may make sense to opt for a platform instead. Databricks is a leading platform for MLOps. In this discussion, I will explain why it is the case, and walk you through Databricks MLOps features. // Bio Maria is an MLOps Tech lead with over 10 years of experience in Data and AI. For the last 8 years, Maria has focused on MLOps and helped to establish MLOps best practices at large corporations. Together with her colleague, she co-founded Marvelous MLOps to share knowledge on MLOps via training, social media posts, and blogs. // Related Links Website: marvelousmlops.io MLOps Course discount code: MLOPS100 for the podcast listeners - https://maven.com/marvelousmlops/mlops-with-databricks?promoCode=MLOPS100 ~~~~~~~~ ✌️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 Maria on LinkedIn: /maria-vechtomovaTimestamps: [00:00] Maria's preferred coffee[00:42] Takeaways[02:48] Why Databricks for MLOps[09:56] Platform Adoption vs Procurement Pain[12:56] Databricks Best Practices[16:57] Feature Store Overview[22:00] Managed system trade-offs[29:15] Databricks Developments and Trends[44:31] Insider Info and Summit[45:47] Data Ownership Pros and Cons[48:08] Data Contracts and Challenges[51:25] MLOps Databricks Book Guide[52:19] Wrap up…

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Making AI Reliable is the Greatest Challenge of the 2020s // Alon Bochman // #312 1:01:37

۶ weeks پیش1:01:37

1:01:37

Making AI Reliable is the Greatest Challenge of the 2020s // MLOps Podcast #312 with Alon Bochman, CEO of RagMetrics. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter Huge shout-out to @RagMetrics for sponsoring this episode! // Abstract Demetrios talks with Alon Bochman, CEO of RagMetrics, about testing in machine learning systems. Alon stresses the value of empirical evaluation over influencer advice, highlights the need for evolving benchmarks, and shares how to effectively involve subject matter experts without technical barriers. They also discuss using LLMs as judges and measuring their alignment with human evaluators. // Bio Alon is a product leader with a fintech and adtech background, ex-Google, ex-Microsoft. Co-founded and sold a software company to Thomson Reuters for $30M, grew an AI consulting practice from 0 to over $ 1 Bn in 4 years. 20-year AI veteran, winner of three medals in model-building competitions. In a prior life, he was a top-performing hedge fund portfolio manager.Alon lives near NYC with his wife and two daughters. He is an avid reader, runner, and tennis player, an amateur piano player, and a retired chess player. // Related Links Website: ragmetrics.ai ~~~~~~~~ ✌️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 Alon on LinkedIn: /alonbochman Timestamps: [00:00] Alon's preferred coffee[00:15] Takeaways[00:47] Testing Multi-Agent Systems[05:55] Tracking ML Experiments[12:28] AI Eval Redundancy Balance[17:07] Handcrafted vs LLM Eval Tradeoffs[28:15] LLM Judging Mechanisms[36:03] AI and Human Judgment[38:55] Document Evaluation with LLM[42:08] Subject Matter Expertise in Co-Pilots[46:33] LLMs as Judges[51:40] LLM Evaluation Best Practices[55:26] LM Judge Evaluation Criteria[58:15] Visualizing AI Outputs[1:01:16] Wrap up…

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Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311 1:01:35

۶ weeks پیش1:01:35

1:01:35

Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // MLOps Podcast #311 with Devansh Devansh, Head of AI at Stealth AI Startup. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractOpen-source AI researcher Devansh Devansh joins Demetrios to discuss grounded AI research, jailbreaking risks, Nvidia’s Gretel AI acquisition, and the role of synthetic data in reducing bias. They explore why deterministic systems may outperform autonomous agents and urge listeners to challenge power structures and rethink how intelligence is built into data infrastructure. // BioThe best meme-maker in Tech. Writer on AI, Software, and the Tech Industry. // Related Links Subscribe to Artificial Intelligence Made Simple: https://artificialintelligencemadesimple.substack.com/https://www.linkedin.com/pulse/alternative-ways-build-ai-models-taoist-devansh-devansh-z9iff/?trackingId=TKvUBldml6rOQUjqam%2B7lA%3D%3D ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Devansh on LinkedIn: /devansh-devansh-516004168 Timestamps:[00:00] Devansh's preferred coffee[01:23] Jailbreaking DeepSeek[02:24] AI Made Simple [07:16] Leveraging AI for Data Insights[10:42] Synthetic Data and LLMs[19:29] AI Experience Design[22:20] Synthetic Data Bias Reduction[26:33] Data Ecosystem Insights[29:50] Moving Intelligence to Data Layer[36:37] Minimizing Model Responsibility[40:04] Workflow vs Generalized Agents[49:24] AI Second-Order Effects[55:26] AI Experience vs Efficiency[1:01:10] Wrap up…

1
GraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // Paco Nathan & Weidong Yang // #310 1:14:01

۷ weeks پیش1:14:01

1:14:01

GraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // MLOps Podcast #310 with Paco Nathan, Principal DevRel Engineer at Senzing & Weidong Yang, CEO of Kineviz. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractExisting BI and big data solutions depend largely on structured data, which makes up only about 20% of all available information, leaving the vast majority untapped. In this talk, we introduce GraphBI, which aims to address this challenge by combining GenAI, graph technology, and visual analytics to unlock the full potential of enterprise data. Recent technologies like RAG (Retrieval-Augmented Generation) and GraphRAG leverage GenAI for tasks such as summarization and Q&A, but they often function as black boxes, making verification challenging. In contrast, GraphBI uses GenAI for data pre-processing—converting unstructured data into a graph-based format—enabling a transparent, step-by-step analytics process that ensures reliability. We will walk through the GraphBI workflow, exploring best practices and challenges in each step of the process: managing both structured and unstructured data, data pre-processing with GenAI, iterative analytics using a BI-focused graph grammar, and final insight presentation. This approach uniquely surfaces business insights by effectively incorporating all types of data. // BioPaco NathanPaco Nathan is a "player/coach" who excels in data science, machine learning, and natural language, with 40 years of industry experience. He leads DevRel for the Entity Resolved Knowledge Graph practice area at Senzing.com and advises Argilla.io, Kurve.ai, KungFu.ai, and DataSpartan.co.uk, and is lead committer for the pytextrank and kglab open source projects. Formerly: Director of Learning Group at O'Reilly Media; and Director of Community Evangelism at Databricks. Weidong YangWeidong Yang, Ph.D., is the founder and CEO of Kineviz, a San Francisco-based company that develops interactive visual analytics based solutions to address complex big data problems. His expertise spans Physics, Computer Science and Performing Art, with significant contributions to the semiconductor industry and quantum dot research at UC, Berkeley and Silicon Valley. Yang also leads Kinetech Arts, a 501(c) non-profit blending dance, science, and technology. An eloquent public speaker and performer, he holds 11 US patents, including the groundbreaking Diffraction-based Overlay technology, vital for sub-10-nm semiconductor production. // Related LinksWebsite: https://www.kineviz.com/Blog: https://medium.com/kinevizWebsite: https://derwen.ai/pacohttps://huggingface.co/pacoidhttps://github.com/ceterihttps://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Weidong on LinkedIn: /yangweidong/Connect with Paco on LinkedIn: /ceteri/…

1
AI Data Engineers - Data Engineering After AI // Vikram Chennai // #309 49:40

۷ weeks پیش49:40

49:40

AI Data Engineers - Data Engineering after AI // MLOps Podcast #309 with Vikram Chennai, Founder/CEO of Ardent AI. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractA discussion of Agentic approaches to Data Engineering. Exploring the benefits and pitfalls of AI solutions and how to design product-grade AI agents, especially in data. // BioSecond Time Founder. 5 years building Deep learning models. Currently, AI Data Engineers // Related LinksWebsite: tryardent.com ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Vikram on LinkedIn: /vikram-chennai/…

1
I Am Once Again Asking "What is MLOps?" // Oleksandr Stasyk // #308 1:07:22

۷ weeks پیش1:07:22

1:07:22

I am once again asking "What is MLOps?" // MLOps Podcast #308 with Oleksandr Stasyk, Engineering Manager, ML Platform of Synthesia. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractWhat does it mean to MLOps now? Everyone is trying to make a killing from AI, everyone wants the freshest technology to show off as part of their product. But what impact does that have on the "journey of the model". Do we still think about how an idea makes it's way to production to make money? How can we get better at it, maybe the answer lies in the ancient "non-AI" past... // BioFor the majority of my career I have been a "full stack" developer with a leaning towards devops and platforms. In the last four years or so, I have worked on ML Platforms. I find that applying good software engineering practises is more important than ever in this AI fueled world. // Related LinksBlogs: https://medium.com/@sashman90/mlops-the-evolution-of-the-t-shaped-engineer-a4d8a24a4042 ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Sash on LinkedIn: /oleksandr-stasyk-5751946b…

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How Sama is Improving ML Models to Make AVs Safer // Duncan Curtis // #307 45:34

۸ weeks پیش45:34

45:34

How Sama is Improving ML Models to Make AVs Safer // MLOps Podcast #307 with Duncan Curtis, SVP of Product and Technology at Sama. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Between Uber’s partnership with NVIDIA and speculation around the U.S.'s President Donald Trump enacting policies that allow fully autonomous vehicles, it’s more important than ever to ensure the accuracy of machine learning models. Yet, the public’s confidence in AVs is shaky due to scary accidents caused by gaps in the tech that Sama is looking to fill.As one of the industry’s top leaders, Duncan Curtis, SVP of Product and Technology at Sama, would be delighted to share how we can improve the accuracy, speed, and cost-efficiency of ML algorithms for AVs. Sama’s machine learning technologies minimize the risk of model failure and lower the total cost of ownership for car manufacturers including Ford, BMW, and GM, as well as four of the five top OEMs and their Tier 1 suppliers. This is especially timely as Tesla is under investigation for crashes due to its Smart Summon feature and Waymo recently had a passenger trapped in one of its driverless taxis. // Bio Duncan Curtis is the SVP of Product at Sama, a leader in de-risking ML models, delivering best-in-class data annotation solutions with our enterprise-strength, experience & expertise, and ethical AI approach. To this leadership role, he brings 4 years of Autonomous Vehicle experience as the Head of Product at Zoox (now part of Amazon) and VP of Product at Aptiv, and 4 years of AI experience as a product manager at Google where he delighted the +1B daily active users of the Play Store and Play Games. // Related Links Website: https://www.sama.com/Tesla is under investigation: https://www.cnn.com/2025/01/07/business/nhtsa-tesla-smart-summon-probe/index.htmlWaymo recently had a passenger trapped: https://www.cbsnews.com/losangeles/news/la-man-nearly-misses-flight-as-self-driving-waymo-taxi-drives-around-parking-lot-in-circles/https://coruzant.com/profiles/duncan-curtis/https://builtin.com/articles/remove-bias-from-machine-learning-algorithmsLook At Your ****ing Data :eyes: // Kenny Daniel // MLOps Podcast #292: https://youtu.be/6EMnkAHmoag ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Luca on LinkedIn: /duncan-curtis Timestamps:[00:00] Duncan's preferred coffee[00:08] Takeaways[01:00] AI Enterprise Focus[04:18] Human-in-the-loop Efficiency[08:42] Edge Cases in AI[14:14] Forward Combat Compatibility Failures[17:30] Specialized Data Annotation Challenges[24:44] SAM for Ring Integration[28:50] Data Bottleneck in AI[31:29] Data Connector Horror Story[33:17] Sama AI Data Annotation[37:20] Cool Business Problems Solved[40:50] AI ROI Framework[45:11] Wrap up…

1
Agents of Innovation: AI-Powered Product Ideation with Synthetic Consumer Testing // Luca Fiaschi // #306 1:02:23

۹ weeks پیش1:02:23

1:02:23

Agents of Innovation: AI-Powered Product Ideation with Synthetic Consumer Testing // MLOps Podcast #306 with Luca Fiaschi, Partner of PyMC Labs. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Traditional product development cycles require extensive consumer research and market testing, resulting in lengthy development timelines and significant resource investment. We've transformed this process by building a distributed multi-agent system that enables parallel quantitative evaluation of hundreds of product concepts. Our system combines three key components: an Agentic innovation lab generating high-quality product concepts, synthetic consumer panels using fine-tuned foundational models validated against historical data, and an evaluation framework that correlates with real-world testing outcomes. We can talk about how this architecture enables rapid concept discovery and digital experimentation, delivering insights into product success probability before development begins. Through case studies and technical deep-dives, you'll learn how we built an AI powered innovation lab that compresses months of product development and testing into minutes - without sacrificing the accuracy of insights. // Bio With over 15 years of leadership experience in AI, data science, and analytics, Luca has driven transformative growth in technology-first businesses. As Chief Data & AI Officer at Mistplay, he led the company’s revenue growth through AI-powered personalization and data-driven pricing. Prior to that, he held executive roles at global industry leaders such as HelloFresh ($8B), Stitch Fix ($1.2B) and Rocket Internet ($1B). Luca's core competencies include machine learning, artificial intelligence, data mining, data engineering, and computer vision, which he has applied to various domains such as marketing, logistics, personalization, product, experimentation and pricing.He is currently a partner at PyMC Labs, a leading data science consultancy, providing insights and guidance on applications of Bayesian and Causal Inference techniques and Generative AI to fortune 500 companies. Luca holds a PhD in AI and Computer Vision from Heidelberg University and has more than 450 citations on his research work. // Related Links Website: https://www.pymc-labs.com/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Luca on LinkedIn: /lfiaschi…

1
Real-Time Forecasting Faceoff: Time Series vs. DNNs // Josh Xi // #305 53:41

۹ weeks پیش53:41

53:41

Real-Time Forecasting Faceoff: Time Series vs. DNNs // MLOps Podcast #305 with Josh Xi, Data Scientist at Lyft. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract In real-time forecasting (e.g. geohash level demand and supply forecast for an entire region), time series-based forecasting methods are widely adopted due to their simplicity and ease of training. This discussion explores how Lyft uses time series forecasting to respond to real-time market dynamics, covering practical tips and tricks for implementing these methods, an in-depth look at their adaptability for online re-training, and discussions on their interpretability and user intervention capabilities. By examining these topics, listeners will understand how time series forecasting can outperform DNNs, and how to effectively use time series forecasting for dynamic market conditions and decision-making applications. // Bio Josh is a data scientist from the Marketplace team at Lyft, working on forecasting and modeling of marketplace signals that power products like pricing and driver incentives. Josh got his PHD in Operations Research in 2013, with minors in Statistics and Economics. Prior to joining Lyft, he worked as a research scientist in the Operations Research Lab at General Motors, focusing on optimization, simulation and forecasting modeling related to vehicle manufacturing, supply chain and car sharing systems. // Related Links Website: https://www.lyft.com/ Real-Time Spatial Temporal Forecasting @ Lyft blog: https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24 ~~~~~~~~ ✌️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 Josh on LinkedIn: /joshxiaominxi…

1
We're All Finetuning Incorrectly // Tanmay Chopra // #304 1:00:30

10 weeks پیش1:00:30

1:00:30

We're All Finetuning Incorrectly // MLOps Podcast #304 with Tanmay Chopra, Founder & CEO of Emissary. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Finetuning is dead. Finetuning is only for style. We've all heard these claims. But the truth is we feel this way because all we've been doing is extended pretraining. I'm excited to chat about what real finetuning looks like - modifying output heads, loss functions and model layers, and it's implications on quality and latency. Happy to dive deeper into how DeepSeek leveraged this real version of finetuning through GRPO and how this is nothing more than a rediscovery of our old finetuning ways. I'm sure we'll naturally also dive into when developing and deploying your specialized models makes sense and the challenges you face when doing so. // Bio Tanmay is a machine learning engineer at Neeva, where he's currently engaged in reimagining the search experience through AI - wrangling with LLMs and building cold-start recommendation systems. Previously, Tanmay worked on TikTok's Global Trust&Safety Algorithms team - spearheading the development of AI technologies to counter violent extremism and graphic violence on the platform across 160+ countries.Tanmay has a bachelor's and master's in Computer Science from Columbia University, with a specialization in machine learning. Tanmay is deeply passionate about communicating science and technology to those outside its realm. He's previously written about LLMs for TechCrunch, held workshops across India on the art of science communication for high school and college students, and is the author of Black Holes, Big Bang and a Load of Salt - a labor of love that elucidated the oft-overlooked contributions of Indian scientists to modern science and helped everyday people understand some of the most complex scientific developments of the past century without breaking into a sweat! // Related Links ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Tanmay on LinkedIn: /tanmayc98…

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From Shiny to Strategic: The Maturation of AI Across Industries // David Cox // #303 40:50

10 weeks پیش40:50

40:50

From Shiny to Strategic: The Maturation of AI Across Industries // MLOps Podcast #303 with David Cox, VP of Data Science; Assistant Director of Research at RethinkFirst; Institute of Applied Behavioral Science. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Shiny new objects are made available to artificial intelligence(AI) practitioners daily. For many who are not AI practitioners, the release of ChatGPT in 2022 was their first contact with modern AI technology. This led to a flurry of funding and excitement around how AI might improve their bottom line. Two years on, the novelty of AI has worn off for many companies but remains a strategic initiative. This strategic nuance has led to two patterns that suggest a maturation of the AI conversation across industries. First, conversations seem to be pivoting from "Are we doing [the shiny new thing]" to serious analysis of the ROI from things built. This reframe places less emphasis on simply adopting new technologies for the sake of doing so and more emphasis on the optimal stack to maximize return relative to cost. Second, conversations are shifting to emphasize market differentiation. That is, anyone can build products that wrap around LLMs. In competitive markets, creating products and functionality that all your competitors can also build is a poor business strategy (unless having a particular thing is industry standard). Creating a competitive advantage requires companies to think strategically about their unique data assets and what they can build that their competitors cannot. // Bio Dr. David Cox can formally lay claim to being a bioethicist (master's degree), a board-certified behavior analyst at the doctoral level, a behavioral economist (post-doc training), and a full-stack data scientist (post-doc training). He has worked in behavioral health for nearly 20 years as a clinician, academic researcher, scholar, technologist, and all-around behavior science junky. He currently works as the Assistant Director of Research for the Institute of Applied Behavioral Science at Endicott College and the VP of Data Science at RethinkFirst. David also likes to write, having published 60+ peer-reviewed articles, book chapters, and a few books. When he's not doing research or building tools at the intersection of artificial intelligence and behavioral health, he enjoys spending time with his wife and two beagles in and around Jacksonville, FL. // Related Links ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with David on LinkedIn: /coxdavidj…

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Streaming Ecosystem Complexities and Cost Management // Rohit Agrawal // #302 48:51

10 weeks پیش48:51

48:51

Streaming Ecosystem Complexities and Cost Management // MLOps Podcast #302 with Rohit Agrawal, Director of Engineering at Tecton. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Demetrios talks with Rohit Agrawal, Director of Engineering at Tecton, about the challenges and future of streaming data in ML. Rohit shares his path at Tecton and insights on managing real-time and batch systems. They cover tool fragmentation (Kafka, Flink, etc.), infrastructure costs, managed services, and trends like using S3 for storage and Iceberg as the GitHub for data. The episode wraps with thoughts on BYOC solutions and evolving data architectures. // Bio Rohit Agrawal is an Engineering Manager at Tecton, leading the Real-Time Execution team. Before Tecton, Rohit was the a Lead Software Engineer at Salesforce, where he focused on transaction processign and storage in OLTP relational databases. He holds a Master’s Degree in Computer Systems from Carnegie Mellon University and a Bachelor’s Degree in Electrical Engineering from the Biria Institute of Technology and Science in Pilani, India. // Related Links ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Rohit on LinkedIn: /agrawalrohit10…

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Fraud Detection in the AI Era // Rafael Sandroni // #301 41:19

10 weeks پیش41:19

41:19

Building Trust Through Technology: Responsible AI in Practice // MLOps Podcast #301 with Rafael Sandroni, Founder and CEO of GardionAI. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractRafael Sandroni shares key insights on securing AI systems, tackling fraud, and implementing robust guardrails. From prompt injection attacks to AI-driven fraud detection, we explore the challenges and best practices for building safer AI. // BioEntrepreneur and problem solver. // Related LinksGardionAI LinkedIn: https://www.linkedin.com/company/guardionai/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Rafael on LinkedIn: /rafaelsandroni Timestamps:[00:00] Rafael's preferred coffee[00:16] Takeaways[01:03] AI Assistant Best Practices[03:48] Siri vs In-App AI[08:44] AI Security Exploration[11:55] Zero Trust for LLMS[18:02] Indirect Prompt Injection Risks[22:42] WhatsApp Banking Risks[26:27] Traditional vs New Age Fraud[29:12] AI Fraud Mitigation Patterns[32:50] Agent Access Control Risks[34:31] Red Teaming and Pentesting[39:40] Data Security Paradox[40:48] Wrap up…

1
Beyond the Matrix: AI and the Future of Human Creativity 55:08

11 weeks پیش55:08

55:08

Beyond the Matrix: AI and the Future of Human Creativity // MLOps Podcast #300 with Fausto Albers, AI Engineer & Community Lead at AI Builders Club. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract Fausto Albers discusses the intersection of AI and human creativity. He explores AI’s role in job interviews, personalized AI assistants, and the evolving nature of human-computer interaction. Key topics include AI-driven self-analysis, context-aware AI systems, and the impact of AI on optimizing human decision-making. The conversation highlights how AI can enhance creativity, collaboration, and efficiency by reducing cognitive load and making intelligent suggestions in real time. // Bio Fausto Albers is a relentless explorer of the unconventional—a techno-optimist with a foundation in sociology and behavioral economics, always connecting seemingly absurd ideas that, upon closer inspection, turn out to be the missing pieces of a bigger puzzle. He thrives in paradox: he overcomplicates the simple, oversimplifies the complex, and yet somehow lands on solutions that feel inevitable in hindsight. He believes that true innovation exists in the tension between chaos and structure—too much of either, and you’re stuck.His career has been anything but linear. He’s owned and operated successful restaurants, served high-stakes cocktails while juggling bottles on London’s bar tops, and later traded spirits for code—designing digital waiters, recommender systems, and AI-driven accounting tools. Now, he leads the AI Builders Club Amsterdam, a fast-growing community where AI engineers, researchers, and founders push the boundaries of intelligent systems.Ask him about RAG, and he’ll insist on specificity—because, as he puts it, discussing retrieval-augmented generation without clear definitions is as useful as declaring that “AI will have an impact on the world.” An engaging communicator, a sharp systems thinker, and a builder of both technology and communities, Fausto is here to challenge perspectives, deconstruct assumptions, and remix the future of AI. // Related Links Website: aibuilders.club ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Fausto on LinkedIn: /stepintoliquid…

1
Efficient GPU infrastructure at LinkedIn // Animesh Singh // MLOps Podcast #299 59:13

11 weeks پیش59:13

59:13

Building Trust Through Technology: Responsible AI in Practice // MLOps Podcast #299 with Animesh Singh, Executive Director, AI Platform and Infrastructure of LinkedIn. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractAnimesh discusses LLMs at scale, GPU infrastructure, and optimization strategies. He highlights LinkedIn's use of LLMs for features like profile summarization and hiring assistants, the rising cost of GPUs, and the trade-offs in model deployment. Animesh also touches on real-time training, inference efficiency, and balancing infrastructure costs with AI advancements. The conversation explores the evolving AI landscape, compliance challenges, and simplifying architecture to enhance scalability and talent acquisition. // BioExecutive Director, AI and ML Platform at LinkedIn | Ex IBM Senior Director and Distinguished Engineer, Watson AI and Data | Founder at Kubeflow | Ex LFAI Trusted AI NA Chair Animesh is the Executive Director leading the next-generation AI and ML Platform at LinkedIn, enabling the creation of the AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. Building Distributed Training Platforms, Machine Learning Pipelines, Feature Pipelines, Metadata engines, etc. Leading the creation of the LinkedIn GAI platform for fine-tuning, experimentation and inference needs. Animesh has more than 20 patents and 50+ publications. Past IBM Watson AI and Data Open Tech CTO, Senior Director, and Distinguished Engineer, with 20+ years experience in the Software industry, and 15+ years in AI, Data, and Cloud Platform. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing, and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, and drove the strategy and execution for Kubeflow, OpenDataHub, and execution in products like Watson OpenScale and Watson Machine Learning. // Related LinksComposable Memory for GPU Optimization // Bernie Wu // Pod #270 - https://youtu.be/ccaDEFoKwko ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Animesh on LinkedIn: /animeshsingh1 Timestamps:[00:00] Animesh's preferred coffee[00:16] Takeaways[02:12] What is working? [07:00] What's not working?[13:40] LLM vs Rexis Efficiency[21:49] GPU Utilization and Architecture[27:32] GPU reliability concerns[36:50] Memory Bottleneck in AI[41:06] Optimizing LLM Checkpointing[46:51] Checkpoint Offloading and Platform Design[54:55] Workflow Divergence Points[58:41] Wrap up…

1
Building Trust Through Technology: Responsible AI in Practice // Allegra Guinan // #298 47:08

12 weeks پیش47:08

47:08

Building Trust Through Technology: Responsible AI in Practice // MLOps Podcast #298 with Allegra Guinan, Co-founder of Lumiera. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractAllegra joins the podcast to discuss how Responsible AI (RAI) extends beyond traditional pillars like transparency and privacy. While these foundational elements are crucial, true RAI success requires deeply embedding responsible practices into organizational culture and decision-making processes. Drawing from Lumiera's comprehensive approach, Allegra shares how organizations can move from checkbox compliance to genuine RAI integration that drives innovation and sustainable AI adoption. // BioAllegra is a technical leader with a background in managing data and enterprise engineering portfolios. Having built her career bridging technical teams and business stakeholders, she's seen the ins and outs of how decisions are made across organizations. She combines her understanding of data value chains, passion for responsible technology, and practical experience guiding teams through complex implementations into her role as co-founder and CTO of Lumiera. // Related LinksWebsite: https://www.lumiera.ai/Weekly newsletter: https://lumiera.beehiiv.com/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with Allegra on LinkedIn: /allegraguinan Timestamps:[00:00] Allegra's preferred coffee[00:14] Takeaways[01:11] Responsible AI principles[03:13] Shades of Transparency[07:56] Effective questioning for clarity [11:17] Managing stakeholder input effectively[14:06] Business to Tech Translation[19:30] Responsible AI challenges[23:59] Successful plan vs Retroactive responsibility[28:38] AI product robustness explained [30:44] AI transparency vs Engagement[34:10] Efficient interaction preferences[37:57] Preserving human essence[39:51] Conflict and growth in life[46:02] Subscribe to Allegra's Weekly Newsletter!…

1
Claude Plays Pokémon - A Conversation with the Creator // David Hershey // #294 46:58

12 weeks پیش46:58

46:58

I Let An AI Play Pokémon! - Claude plays Pokémon Creator // MLOps Podcast #295 with David Hershey, Member of Technical Staff at Anthropic. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractDemetrios chats with David Hershey from Anthropic's Applied AI team about his agent-powered Pokémon project using Claude. They explore agent frameworks, prompt optimization vs. fine-tuning, and AI's growing role in software, legal, and accounting fields. David highlights how managed AI platforms simplify deployment, making advanced AI more accessible. // BioDavid Hershey devoted most of his career to machine learning infrastructure and trying to abstract away the hairy systems complexity that gets in the way of people building amazing ML applications. // Related LinksWebsite: https://www.davidhershey.com/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with David on LinkedIn: /david-hershey-458ab081…

1
From Rules to Reasoning Engines // George Mathew // #296 1:05:26

13 weeks پیش1:05:26

1:05:26

From Rules to Reasoning Engines // MLOps Podcast #297 with George Mathew, Managing Director at Insight Partners. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractGeorge Mathew (Insight Partners) joins Demetrios to break down how AI and ML have evolved over the past few years and where they’re headed. He reflects on the major shifts since his last chat with Demetrios, especially how models like ChatGPT have changed the game. George dives into "generational outcomes"—building companies with lasting impact—and the move from rule-based software to AI-driven reasoning engines. He sees AI becoming a core part of all software, fundamentally changing business operations. The chat covers the rise of agent-based systems, the importance of high-quality data, and recent breakthroughs like Deep SEQ, which push AI reasoning further. They also explore AI’s future—its role in software, enterprise adoption, and everyday life. // BioGeorge Mathew is a Managing Director at Insight Partners focused on venture stage investments in AI, ML, Analytics, and Data companies as they are establishing product/market fit. He brings 20+ years of experience developing high-growth technology startups including most recently being CEO of Kespry. Prior to Kespry, George was President & COO of Alteryx where he scaled the company through its IPO (AYX). Previously he held senior leadership positions at SAP and salesforce.com. He has driven company strategy, led product management and development, and built sales and marketing teams. George holds a Bachelor of Science in Neurobiology from Cornell University and a Masters in Business Administration from Duke University, where he was a Fuqua Scholar. // Related LinksWebsite: https://www.insightpartners.com/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin 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: /dpbrinkmConnect with George on LinkedIn: /gmathew…

21 weeks پیش51:33

51:33

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! // Abstract Qualcomm® 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. // Bio Krishna 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 its 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 Links Website: 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/…

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Towards Observability for ML Pipelines // Shreya Shankar // MLOps Coffee Sessions #75