PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274

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Wyatt Flores’s Red Dirt Roots 43:43

20 روز پیش43:43

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43:43

Wyatt Flores is a 23-year-old singer-songwriter from Stillwater, Oklahoma, who’s making a name for himself in the Red Dirt music scene, building on the musical legacy of his home state. He grew up on a ranch in a working-class family where he was surrounded by musicians, often hearing them play cowboy songs around a campfire. His father, a drummer, built him a stage in the backyard when he recognized his talent, and his Uncle Bobby taught him how to play guitar. Now Wyatt is playing in front of thousands of fans, singing at the Grand Ole Opry, and writing songs that are winning audiences with their honesty, heart, and vulnerability. His debut album, Welcome to the Plains, explores the rough—and sometimes violent—side of growing up in rural Oklahoma, but it also shows a talent for storytelling and a wry sense of humor. Sid talks to Wyatt about why he’s so happy to be back in Stillwater after a couple of years in Nashville, the mental health struggles he’s openly shared with his fans, the family member he wants to have on his podcast, and why his favorite food is a breakfast burrito. Learn more about your ad choices. Visit podcastchoices.com/adchoices…

حدود یک سال پیش 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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PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274

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1
Bridging the Gap Between AI and Business Data // Deepti Srivastava // #325 57:13

۳ روز پیش57:13

57:13

Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract I’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work. // Bio Deepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure. As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle. // Related Links Website: https://www.snowleopard.ai/ AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ ~~~~~~~~ ✌️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 Deepti on LinkedIn: /thedeepti/ Timestamps: [00:00] Deepti's preferred coffee [00:49] MLflow vs Kubeflow Debate [04:58] GenAI Data Integration Challenges [09:02] GenAI Sidecar Spicy Takes [14:07] Troubleshooting LLM Hallucinations [19:03] AI Overengineering and Hype [25:06] Self-Serve Analytics Governance [33:29] Dashboards vs Data Quality [37:06] Agent Database Context Control [43:00] LLM as Orchestrator [47:34] Tool Call Ownership Clarification [51:45] MCP Server Challenges [56:52] Wrap up…

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The Creator of FastAPI’s Next Chapter // Sebastián Ramírez // #324 1:09:37

۶ روز پیش1:09:37

1:09:37

The Creator of FastAPI’s Next Chapter // MLOps Podcast #324 with Sebastián Ramírez, Developer at FastAPI Labs. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract The creator of FastAPI is back with a new chapter—FastAPI Cloud. From building one of the most loved dev tools to launching a company, Sebastián Ramírez shares how open source, developer experience, and a dash of humor are shaping the future of APIs. // Bio Sebastián Ramírez (also known as Tiangolo) is the creator of FastAPI, Typer, SQLModel, Asyncer, and several other widely used open source tools.He has collaborated with companies and teams around the world—from Latin America to the Middle East, Europe, and the United States—building a range of products and custom solutions focused on APIs, data processing, distributed systems, and machine learning. Today, he works full time on FastAPI and its growing ecosystem. // Related Links Website: https://tiangolo.com/ FastAPI: https://fastapi.tiangolo.com/ FastAPI Cloud: https://fastapicloud.com/ FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96 - https://youtu.be/NpvRhZnkEFg ~~~~~~~~ ✌️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 Tiangolo on LinkedIn: /tiangolo Timestamps: [00:00] Sebastián's preferred coffee [00:15] Takeaways [01:43] Why Pydantic is Awesome [06:47] ML Background and FastAPI [10:44] NASA FastAPI Emojis [15:21] FastAPI Cloud Journey [26:07] FastAPI Cloud Open-Source Balance [31:45] Basecamp Design Philosophy [35:30] AI Abstraction Strategies [42:56] Engineering vs Developer Experience [51:40] Dogfooding and Docs Strategy [59:44] Code Simplicity and Trust [1:04:26] Scaling Without Losing Vision [1:08:20] FastAPI Cloud Signup [1:09:23] Wrap up…

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Everything Hard About Building AI Agents Today 47:02

10 روز 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…

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

12 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…

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

13 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…

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

17 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?…

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

20 روز پیش53:06

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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…

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

24 روز پیش50:29

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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…

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

۴ weeks 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…

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

۵ weeks 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)…

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

۵ weeks 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…

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

۶ 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…

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

۶ 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

۷ 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

۷ 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…

MLOps.community

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

۸ 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/…

MLOps.community

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

۸ 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/…

MLOps.community

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

۹ 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…

MLOps.community

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

۹ 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…

MLOps.community

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

10 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…

MLOps.community

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

10 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…

MLOps.community

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

11 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…

MLOps.community

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

11 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

11 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

12 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…

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

12 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…

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

12 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…

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

13 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!…

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

13 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…

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

14 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…

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Collective Memory for AI on Decentralized Knowledge Graph // Tomaž Levak // #285 53:24

21 weeks پیش53:24

53:24

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. // Abstract The 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 details 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 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. // Bio Tomaz 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 Links Website: 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/…

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Efficient Deployment of Models at the Edge // Krishna Sridhar // #284 51:33

22 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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Real World AI Agent Stories // Zach Wallace // #283 47:07

23 weeks پیش47:07

47:07

MLOps.community

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Machine Learning, AI Agents, and Autonomy // Egor Kraev // #282 1:05:20

24 weeks پیش1:05:20

1:05:20

Since three years, Egor is bringing the power of AI to bear at Wise , across domains as varied as trading algorithms for Treasury, fraud detection, experiment analysis and 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 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 is 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/…

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Re-Platforming Your Tech Stack // Michelle Marie Conway & Andrew Baker // #281 51:14

24 weeks پیش51:14

51:14

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 Conway Michelle 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 Andrew 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 Links Website: 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…

MLOps.community

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Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 57:33

26 weeks پیش57:33

57:33

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 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 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 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 Masters 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/…

MLOps.community

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Unleashing Unconstrained News Knowledge Graphs to Combat Misinformation // Robert Caulk // #279 1:15:24

26 weeks پیش1:15:24

1:15:24

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 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…

MLOps.community

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LLM Distillation and Compression // Guanhua Wang // #278 49:47

27 weeks پیش49:47

49:47

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…

MLOps.community

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AI's Next Frontier // Aditya Naganath // #277 57:30

28 weeks پیش57:30

57:30

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/…

MLOps.community

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PyTorch for Control Systems and Decision Making // Vincent Moens // #276 56:39

29 weeks پیش56:39

56:39

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/…

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AI-Driven Code: Navigating Due Diligence & Transparency in MLOps // Matt van Itallie // #275 57:01

29 weeks پیش57:01

57:01

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 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 Matt on LinkedIn: https://www.linkedin.com/in/mvi/…

MLOps.community

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PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274 57:44

30 weeks پیش57:44

57:44

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…

MLOps.community

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LLMs to agents: The Beauty & Perils of Investing in GenAI // VC Panel // Agents in Production 33:24

30 weeks پیش33:24

33:24

//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…

MLOps.community

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We Can All Be AI Engineers and We Can Do It with Open Source Models // Luke Marsden // #273 51:08

31 weeks پیش51:08

51:08

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 Links Website: 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/…

MLOps.community

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Exploring AI Agents: Voice, Visuals, and Versatility // Panel // Agents in Production 28:58

31 weeks پیش28:58

28:58

//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…

MLOps.community

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GenAI Traffic: Why API Infrastructure Must Evolve... Again // Erica Hughberg // #296 1:06:24

14 weeks پیش1:06:24

1:06:24

GenAI Traffic: Why API Infrastructure Must Evolve... Again // MLOps Podcast #295 with Erica Hughberg, Community Advocate at Tetrate.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter

MLOps.community

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The Unbearable Lightness of Data // Rohit Krishnan // #295 54:16

15 weeks پیش54:16

54:16

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 // AbstractRohit 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.// BioBuilding products, writing, messing around with AI pretty much everywhere// Related LinksWebsite: www.strangeloopcanon.comIn 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/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: /rkris…

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Kubernetes, AI Gateways, and the Future of MLOps // Alexa Griffith // #294 51:43

15 weeks پیش51:43

51:43

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 LinksWebsite: 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…

MLOps.community

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Future of Software, Agents in the Enterprise, and Inception Stage Company Building // Eliot Durbin // #293 54:26

16 weeks پیش54:26

54:26

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 // AbstractKey lessons for founders that 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 share's key learnings and questions for founders starting out. // BioEliot is a General Partner @ boldstart ventures since it's founding in 2010. boldstart 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 LinksWebsite: 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…

MLOps.community

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The Agent Exchange: Practitioner Insights 48:17

16 weeks پیش48:17

48:17

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

16 weeks پیش53:56

53:56

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…

MLOps.community

1
Getting to Grips with Web Agents 45:52

17 weeks پیش45:52

45:52

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…

MLOps.community

1
The Challenge with Voice Agents 47:37

17 weeks پیش47:37

47:37

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…

MLOps.community

1
The Agent Landscape - Lessons Learned Putting Agents Into Production 1:08:40

18 weeks پیش1:08:40

1:08:40

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…

MLOps.community

1
Evolving Workflow Orchestration // Alex Milowski // #291 1:14:34

18 weeks پیش1:14:34

1:14:34

Alex Milowski is a researcher, developer, entrepreneur , mathematician, and computer scientist .Evolving Workflow Orchestration // MLOps Podcast #291 with Alex Milowski, Entrepreneur and Computer Scientist.// AbstractThere seems to be a shift from workflow languages to code - mostly annotation pythons - 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 a external workflow langauge. Both had a batch task executor on K8s but at MicroByre, we had human and robot in the loop workflows.// BioDr. 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 in research.// MLOps Swag/Merch https://shop.mlops.community/ // 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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Alex on LinkedIn: https://www.linkedin.com/in/alexmilowski/…

MLOps.community

1
Insights from Cleric: Building an Autonomous AI SRE // Willem Pienaar // #290 55:57

19 weeks پیش55:57

55:57

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.// AbstractIn 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.// BioWillem 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.// MLOps Swag/Merch https://shop.mlops.community/ // 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/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Willem on LinkedIn: https://www.linkedin.com/in/willempienaar/…

MLOps.community

1
Robustness, Detectability, and Data Privacy in AI // Vinu Sankar Sadasivan // #289 52:59

19 weeks پیش52:59

52:59

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. // Abstract Recent 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. // Bio Vinu 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 Links Website: 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/…

MLOps.community

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AI & Aliens: New Eyes on Ancient Questions // Richard Cloete // #288 47:58

20 weeks پیش47:58

47:58

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. // Abstract Demetrios 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. // Bio Richard 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 Links Website: 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/…

MLOps.community

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Real LLM Success Stories: How They Actually Work // Alex Strick van Linschoten // #287 49:54

20 weeks پیش49:54

49:54

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. // Abstract Alex 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. // Bio Alex 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 Links Website: 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…

MLOps.community

1
Navigating Machine Learning Careers: Insights from Meta to Consulting // Ilya Reznik // #286 1:00:36

21 weeks پیش1:00:36

1:00:36

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. // Abstract Ilya 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. // Bio Ilya 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 Links Website: 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/…

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MLOps.community « » PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274

PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274

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PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274