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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:
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// 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

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

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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/

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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

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.

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// 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/

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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!

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

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// 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/

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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

Varant Zanoyan is the Co-founder & CEO at Zipline AI, working on building a next-generation AI/ML infrastructure platform that streamlines data pipelines, model deployment, observability, and governance to accelerate enterprise AI development.

Nikhil Simha Raprolu is the Co-founder & CTO at Zipline AI, focused on architecting and scaling the company’s AI data platform — extending the open-source Chronon engine into a developer-friendly system that simplifies building and operating production AI applications.

Real-time features, AI search, Agentic similarities, Varant Zanoyan & Nikhil Simha Raprolu // MLOps Podcast #354

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And huge thanks to Chroma for hosting us in their recording studio
// Abstract
Feature stores might be the wrong abstraction. Varant Zanoyan and Nikhil Simha Raprolu explain why Cronon ditched “store-first” thinking and focused on compute, orchestration, and real-time correctness—born at Airbnb, battle-tested with Stripe. If embeddings, agents, and real-time ML feel painful, this episode explains why.

// Related Links
Website: https://zipline.ai/

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Connect with Varant on LinkedIn: /vzanoyan/
Connect with Nikhil on LinkedIn: /nikhilsimha/

Timestamps:
[00:00] Feature Platform Insights
[02:00] Zipline and Feature Stores
[05:19] Cronon and Zipline Origins
[10:49] Feast and Feather Comparison
[13:27] Open source challenges
[20:52] Zipline and Iceberg Integration
[23:54] Airbnb Agent Systems
[28:16] Features vs Embeddings
[29:07] Wrap up