MLOps.community

Demetrios

Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

  1. Physical AI: Teaching Machines to Understand the Real World

    1D AGO

    Physical AI: Teaching Machines to Understand the Real World

    Nick Gillian is the Co-Founder and CTO at Archetype AI, working on physical AI foundation models that understand and reason over real-world sensor data.Physical AI: Teaching Machines to Understand the Real World // MLOps Podcast #360 with Nick Gillian, Co-Founder and CTO of Archetype AI Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractAs AI moves beyond the cloud and simulation, the next frontier is Physical AI: systems that can perceive, understand, and act within real-world environments in real time. In this conversation, Nick Gillian, Co-Founder and CTO of Archetype AI, explores what it actually takes to turn raw sensor and video data into reliable, deployable intelligence.Drawing on his experience building Google’s Soli and Jacquard and now leading development of Newton, a foundational model for Physical AI, Nick discusses how real-time physical understanding changes what’s possible across safety monitoring, infrastructure, and human–machine interaction. He’ll share lessons learned translating advanced research into products that operate safely in dynamic environments, and why many organizations underestimate the challenges and opportunities of AI in the physical world.// BioNick Gillian, Ph.D., is Co-Founder and CTO of Archetype AI with over 15 years of experience turning advanced AI and interaction research into real-world products. At Archetype, he leads the AI and engineering teams behind Newton—a first-of-its-kind Physical AI foundational model that can perceive, understand, and reason about the physical world. Before co-founding Archetype, Nick was a Senior Staff Machine Learning Engineer at Google and a researcher at MIT, where he developed AI and ML methods for real-time sensor understanding. At Google’s Advanced Technology and Projects group, he led machine learning research that powered breakthrough products like Soli radar and Jacquard, and helped advance sensing algorithms across Pixel, Nest, and wearable devices.// Related LinksWebsite: https://www.archetypeai.io/https://www.archetypeai.io/blog/timefusion-newton https://www.nature.com/articles/s41598-023-44714-2https://www.youtube.com/watch?v=Pow4utY9teU https://www.youtube.com/watch?v=uE0jjdzwe9w https://arxiv.org/abs/2410.14724 Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️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 Nick on LinkedIn: /nick-gillian-b27b1094/

    52 min
  2. Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth

    4D AGO

    Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth

    Kris Beevers is the CEO at NetBox Labs, working on turning NetBox into the system of record and automation backbone for modern and AI-driven infrastructure. Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth // MLOps Podcast #359 with Kris Beevers, CEO of NetBox Labs Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter MLOps GPU Guide: https://go.mlops.community/gpuguide // Abstract Hundreds of neocloud operators and "AI Factory" builders have emerged to serve the insatiable demand for AI infrastructure. These teams are compressing the design, build, deploy, operate, scale cycle of their infrastructures down to months, while managing massive footprints with lean teams. How? By applying modern intent-driven infrastructure automation principles to greenfield deployments. We'll explore how these teams carry design intent through to production, and how operating and automating around consistent infrastructure data is compressing "time to first train". // Bio Kris Beevers is the Co-founder and CEO of NetBox Labs. NetBox is used by nearly every Neocloud and AI datacenter to manage their networks and infrastructure. Kris is an engineer at heart and by background, and loves the leverage infrastructure innovation creates to accelerate technology and empower engineers to do their best work. A serial entrepreneur, Kris has founded and helped lead multiple other successful businesses in the internet and network infrastructure. Most recently, he co-founded and led NS1, which was acquired by IBM in 2023. He holds a Ph.D. in Computer Science from Rensselaer Polytechnic Institute and is based in New Jersey. // Related Links Website: https://netboxlabs.com/ Coding Agents Conference: https://luma.com/codingagents ~~~~~~~~ ✌️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 Kris on LinkedIn: /beevek/ Timestamps: [00:00] Observability and Delta Analysis [00:26] New World Exploration [04:06] Bottlenecks in AI Infrastructure [13:37] Data Center Optimization Challenges [19:58] Tech Stack Breakdown [25:26] Data Center Design Principles [31:32] Constraints and Automation in Design [40:00] Complexity in Data Centers [45:02] GPU Cloud Landscape [50:24] Data Centers in Containers [57:45] Observability Beyond Software [1:04:43] Tighter Integrations vs NetBox [1:06:47] Wrap up

    1h 7m
  3. Cracking the Black Box: Real-Time Neuron Monitoring & Causality Traces

    JAN 27

    Cracking the Black Box: Real-Time Neuron Monitoring & Causality Traces

    Mike Oaten is the Founder and CEO of TIKOS, working on building AI assurance, explainability, and trustworthy AI infrastructure, helping organizations test, monitor, and govern AI models and systems to make them transparent, fair, robust, and compliant with emerging regulations. Cracking the Black Box: Real-Time Neuron Monitoring & Causality Traces // MLOps Podcast #358 with Mike Oaten, Founder and CEO of TIKOS Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract As AI models move into high-stakes environments like Defence and Financial Services, standard input/output testing, evals, and monitoring are becoming dangerously insufficient. To achieve true compliance, MLOps teams need to access and analyse the internal reasoning of their models to achieve compliance with the EU AI Act, NIST AI RMF, and other requirements. In this session, Mike introduces the company's patent-pending AI assurance technology that moves beyond statistical proxies. He will break down the architecture of the Synapses Logger, a patent-pending technology that embeds directly into the neural activation flow to capture weights, activations, and activation paths in real-time. // Bio Mike Oaten serves as the CEO of TIKOS, leading the company’s mission to progress trustworthy AI through unique, high-performance AI model assurance technology. A seasoned technical and data entrepreneur, Mike brings experience from successfully co-founding and exiting two previous data science startups: Riskopy Inc. (acquired by Nasdaq-listed Coupa Software in 2017) and Regulation Technologies Limited (acquired by mnAi Data Solutions in 2022). Mike's expertise spans data, analytics, and ML product and governance leadership. At TIKOS, Mike leads a VC-backed team developing technology to test and monitor deep-learning models in high-stakes environments, such as defence and financial services, so they comply with the stringent new laws and regulations. // Related Links Website: https://tikos.tech/ LLM guardrails: https://medium.com/tikos-tech/your-llm-output-is-confidently-wrong-heres-how-to-fix-it-08194fdf92b9 Model Bias: https://medium.com/tikos-tech/from-hints-to-hard-evidence-finally-how-to-find-and-fix-model-bias-in-dnns-2553b072fd83 Model Robustness: https://medium.com/tikos-tech/tikos-spots-neural-network-weaknesses-before-they-fail-the-iris-dataset-b079265c04da GPU Optimisation: https://medium.com/tikos-tech/400x-performance-a-lightweight-open-source-python-cuda-utility-to-break-vram-barriers-d545e5b6492f Hyperbolic GPU Cloud: app.hyperbolic.ai. Coding Agents Conference: https://luma.com/codingagents ~~~~~~~~ ✌️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 Mike on LinkedIn: /mike-oaten/ Timestamps: [00:00] Regulations as Opportunity [00:25] Regulation Compliance Fun [02:49] AI Act Layers Explained [05:19] Observability in Systems vs ML [09:05] Risk Transfer in AI [11:26] LLMs and Model Approval [14:53] LLMs in Finance [17:17] Hyperbolic GPU Cloud Ad [18:16] Stakeholder Alignment and Tech [22:20] AI in Regulated Environments [28:55] Autonomous Boat Regulations [34:20] Data Compliance Mapping [39:11] Data Capture Strategy [41:13] EU AI Act Insights [44:52] Wrap up [45:45] Join the Coding Agents Conference!

    47 min
  4. A Playground for AI/ML Engineers

    JAN 23

    A Playground for AI/ML Engineers

    Paulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, working on AI-powered creator and learning experiences, including intelligent tutoring, content automation, and multilingual localization at scale. Join us at Coding Agents: The AI Driven Developer Conference - https://luma.com/codingagents MLOps GPU Guide: ⁠https://go.mlops.community/gpuguide Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract “Agent as a product” sounds like hype, until Hotmart turns creators’ content into AI businesses that actually work. // Bio Paulo Vasconcellos is the Principal Data Scientist for Generative AI Products at Hotmart, where he leads efforts in applied AI, machine learning, and generative technologies to power intelligent experiences for creators and learners. He holds an MSc in Computer Science with a focus on artificial intelligence and is also a co-founder of Data Hackers, a prominent data science and AI community in Brazil. Paulo regularly speaks and publishes on topics spanning data science, ML infrastructure, and AI innovation. // Related LinksWebsite: paulovasconcellos.com.br Coding Agent - Virtual Conference: https://home.mlops.community/home/events/coding-agents-virtual ~~~~~~~~ ✌️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/] MLOps GPU Guide: https://go.mlops.community/gpuguide Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Paulo on LinkedIn: /paulovasconcellos/ Timestamps: [00:00] Hotmart Data Science Challenges [02:38] LLMs vs spaCy [11:38] Use Cases in Production [19:04] Coding Agents Virtual Conference Announcement! [29:27] ML to AI Product Shift [34:49] Tool-Augmented Agent Approach [38:28] MLOps GPU Guide [41:24] AI Use Cases at Hotmart [49:34] Agent Tool Access Explained [51:04] MLOps Community Gratitude [53:22] Wrap up

    55 min
  5. How Universal Resource Management Transforms AI Infrastructure Economics

    JAN 20

    How Universal Resource Management Transforms AI Infrastructure Economics

    Wilder Lopes is the CEO and Founder of Ogre.run, working on AI-driven dependency resolution and reproducible code execution across environments.How Universal Resource Management Transforms AI Infrastructure Economics // MLOps Podcast #357 with Wilder Lopes, CEO / Founder of Ogre.runJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter // AbstractEnterprise organizations face a critical paradox in AI deployment: while 52% struggle to access needed GPU resources with 6-12 month waitlists, 83% of existing CPU capacity sits idle. This talk introduces an approach to AI infrastructure optimization through universal resource management that reshapes applications to run efficiently on any available hardware—CPUs, GPUs, or accelerators.We explore how code reshaping technology can unlock the untapped potential of enterprise computing infrastructure, enabling organizations to serve 2-3x more workloads while dramatically reducing dependency on scarce GPU resources. The presentation demonstrates why CPUs often outperform GPUs for memory-intensive AI workloads, offering superior cost-effectiveness and immediate availability without architectural complexity.// BioWilder Lopes is a second-time founder, developer, and research engineer focused on building practical infrastructure for developers. He is currently building Ogre.run, an AI agent designed to solve code reproducibility.Ogre enables developers to package source code into fully reproducible environments in seconds. Unlike traditional tools that require extensive manual setup, Ogre uses AI to analyze codebases and automatically generate the artifacts needed to make code run reliably on any machine. The result is faster development workflows and applications that work out of the box, anywhere.// Related LinksWebsite: https://ogre.runhttps://lopes.aihttps://substack.com/@wilderlopes https://youtu.be/YCWkUub5x8c?si=7RPKqRhu0Uf9LTql ~~~~~~~~ ✌️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 Wilder on LinkedIn: /wilderlopes/Timestamps:[00:00] Secondhand Data Centers Challenges[00:27] AI Hardware Optimization Debate[03:40] LLMs on Older Hardware[07:15] CXL Tradeoffs[12:04] LLM on CPU Constraints[17:07] Leveraging Existing Hardware[22:31] Inference Chips Overview[27:57] Fundamental Innovation in AI[30:22] GPU CPU Combinations[40:19] AI Hardware Challenges[43:21] AI Perception Divide[47:25] Wrap up

    48 min
  6. Conversation with the MLflow Maintainers

    JAN 16

    Conversation with the MLflow Maintainers

    Corey Zumar is a Product Manager at Databricks, working on MLflow and LLM evaluation, tracing, and lifecycle tooling for generative AI. Jules Damji is a Lead Developer Advocate at Databricks, working on Spark, lakehouse technologies, and developer education across the data and AI community. Danny Chiao is an Engineering Leader at Databricks, working on data and AI observability, quality, and production-grade governance for ML and agent systems. MLflow Leading Open Source // MLOps Podcast #356 with Databricks' Corey Zumar, Jules Damji, and Danny Chiao Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter Shoutout to Databricks for powering this MLOps Podcast episode. // Abstract MLflow isn’t just for data scientists anymore—and pretending it is is holding teams back. Corey Zumar, Jules Damji, and Danny Chiao break down how MLflow is being rebuilt for GenAI, agents, and real production systems where evals are messy, memory is risky, and governance actually matters. The takeaway: if your AI stack treats agents like fancy chatbots or splits ML and software tooling, you’re already behind. // Bio Corey Zumar Corey has been working as a Software Engineer at Databricks for the last 4 years and has been an active contributor to and maintainer of MLflow since its first release. Jules Damji Jules is a developer advocate at Databricks Inc., an MLflow and Apache Spark™ contributor, and Learning Spark, 2nd Edition coauthor. He is a hands-on developer with over 25 years of experience. He has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/LoudCloud, VeriSign, ProQuest, Hortonworks, Anyscale, and Databricks, building large-scale distributed systems. He holds a B.Sc. and M.Sc. in computer science (from Oregon State University and Cal State, Chico, respectively) and an MA in political advocacy and communication (from Johns Hopkins University) Danny Chiao Danny is an engineering lead at Databricks, leading efforts around data observability (quality, data classification). Previously, Danny led efforts at Tecton (+ Feast, an open source feature store) and Google to build ML infrastructure and large-scale ML-powered features. Danny holds a Bachelor’s Degree in Computer Science from MIT. // Related Links Website: https://mlflow.org/ 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 Corey on LinkedIn: /corey-zumar/ Connect with Jules on LinkedIn: /dmatrix/ Connect with Danny on LinkedIn: /danny-chiao/ Timestamps: [00:00] MLflow Open Source Focus [00:49] MLflow Agents in Production [00:00] AI UX Design Patterns [12:19] Context Management in Chat [19:24] Human Feedback in MLflow [24:37] Prompt Entropy and Optimization [30:55] Evolving MLFlow Personas [36:27] Persona Expansion vs Separation [47:27] Product Ecosystem Design [54:03] PII vs Business Sensitivity [57:51] Wrap up

    58 min
  7. Leadership on AI

    JAN 13

    Leadership on AI

    Euro Beinat is the Global Head of AI and Data Science at Prosus Group, working on scaling AI-driven tools and agent-based systems across Prosus’s global portfolio, deploying internal assistants like Toqan and generative AI platforms such as PlusOne, and building initiatives like AI House Amsterdam and interdisciplinary AI residencies to explore intent-driven AI and strengthen Europe’s AI ecosystem. Mert Öztekin is the Chief Technology Officer at Just Eat Takeaway.com, working on advancing the company’s platform with AI-driven ordering and personalised user experiences, scaling cloud and generative AI tooling for engineering productivity, and exploring innovative delivery technologies like automation to make ordering and delivery more seamless. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter MLOps GPU Guide: https://go.mlops.community/gpuguide // Abstract Agents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated. // Bio Euro Beinat Euro is a technology executive and entrepreneur specializing in data science, machine learning, and AI. He works with global corporations and startups to build data- and ML-driven products and businesses. His current focus is on Generative AI and the use of AI as a tool for invention and innovation. Mert Öztekin Mert is the current Chief Technology Officer at Just Eat Takeaway.com with previous experience as a CTO at Delivery Hero Germany GmbH, Director of Engineering at Delivery Hero, and IT Manager at yemeksepeti.com. They have a background in software engineering, system-business analysis, and project management, with a master's degree in Computer Engineering. Mert has also worked as an IT Project Team Lead and has experience in managing mobile teams and global expansions in the online food ordering industry. // Related Links Website: https://www.prosus.com/ Website: https://justeattakeaway.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/] MLOps GPU Guide: https://go.mlops.community/gpuguide Connect with Demetrios on LinkedIn: /dpbrinkm Connect with Euro on LinkedIn: /eurobeinat/ Connect with Mert on LinkedIn: /mertoztekin/ Timestamps: [00:00] AI Transformation Challenges [00:29] AI Productivity [04:30] Developer Tool Freedom [09:40] AI Alignment Bottleneck [22:17] Exploring Agent Potential [25:59] Governance of AI Agents [33:24] Shadow AI Governance [40:57] AI Budgeting for Growth [46:27] MLOps GPU Guide announcement!

    47 min
  8. Computers that Think and Take Actions for You

    JAN 2

    Computers that Think and Take Actions for You

    Zengyi Qin is the Founder of the OpenAGI Foundation, working on computer-use models and open, agent-centric AI infrastructure. Computers that Think and Take Actions for You, Zengy Qin // MLOps Podcast #355 Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter MLOps Merch: https://shop.mlops.community/ // Abstract What if the computer itself can think and take actions for you? You just give it a goal, and it performs every click, type, drag, and gets work done across the desktop and web. In this talk, Zengyi reveals the breakthrough technology that his company OpenAGI is developing: AI that can use computers like humans do. He talks about how his team developed the model, why it outperforms similar models from OpenAI and Google, and its wide use cases across different domains. // Related Links Website: https://www.qinzy.tech/ ~~~~~~~~ ✌️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 Zengyi on LinkedIn: /qinzy/ Timestamps: [00:00] AI and Human Interaction [00:30] Zengyi's story [08:19] Why Expensive Models Lost [06:30] Bigger Models Are Lazy [10:24] Training Computer-Use vs LLMs [13:53] World Models and Sandboxes [19:42] Dealing with Non-Stationary States [23:56] Training with Software [26:44] Sandbox Training Process [41:33] Infrastructure for Computer Models [44:36] Wrap up

    45 min
4.6
out of 5
23 Ratings

About

Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

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