MLOps Community Podcast
# AI Agents
# AI Engineer
# AI agents in production
# AI agent usecase
# System Design
How Universal Resource Management Transforms AI Infrastructure Economics
Enterprise 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.
Wilder Lopes & Demetrios Brinkmann · Jan 20th, 2026
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Corey Zumar, Danny Chiao, Jules Damji & 1 content:more content:speaker · Jan 16th, 2026
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.
# Agents in Production
# Open Source
# MLflow
# Databricks
Zengy Qin & Demetrios Brinkmann · Jan 2nd, 2026
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.
# AI Agents
# Robotics
# OpenAGI Foundation
Varant Zanoyan, Nikhil Simha & Demetrios Brinkmann · Dec 28th, 2025
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.
# AI Search
# AI Agents
# Zipline AI
Chiara Caratelli, Alex Salazar & Demetrios Brinkmann · Dec 23rd, 2025
Agents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.
# Prompt Engineering
# AI Agents
# AI Engineer
# AI agents in production
# AI agent usecase
# system design
Jonathan Wall & Demetrios Brinkmann · Dec 19th, 2025
Everyone’s arguing about agents. Jonathan Wall says the real fight is about sandboxes, isolation, and why most “agent platforms” are doing it wrong.
# AI Agents
# Sandboxes
# Runloop.AI
Simba Khadder & Demetrios Brinkmann · Dec 16th, 2025
Feature stores aren’t dead — they were just misunderstood.
Simba Khadder argues the real bottleneck in agents isn’t models, it’s context, and why Redis is quietly turning into an AI data platform. Context engineering matters more than clever prompt hacks.
# Context Engineering
# Featureform
# Redis
Satish Bhambri & Demetrios Brinkmann · Dec 12th, 2025
Satish Bhambri is a Sr Data Scientist at Walmart Labs, working on large-scale recommendation systems and conversational AI, including RAG-powered GroceryBot agents, vector-search personalization, and transformer-based ad relevance models.
# AgenticRAG
# AI Engineer
# AI Agents
Zack Reneau-Wedeen & Demetrios Brinkmann · Dec 10th, 2025
Sierra’s Zack Reneau-Wedeen claims we’re building AI all wrong and that “context engineering,” not bigger models, is where the real breakthroughs will come from. In this episode, he and Demetrios Brinkmann unpack why AI behaves more like a moody coworker than traditional software, why testing it with real-world chaos (noise, accents, abuse, even bad mics) matters, and how Sierra’s simulations and model “constellations” aim to fix the industry’s reliability problems. They even argue that decision trees are dead replaced by goals, guardrails, and speculative execution tricks that make voice AI actually usable. Plus: how Sierra trains grads to become product-engineering hybrids, and why obsessing over customers might be the only way AI agents stop disappointing everyone.
# AI Systems
# Agent Simulations
# AI Voice Agent
Spencer Reagan & Demetrios Brinkmann · Dec 5th, 2025
Spencer Reagan thinks it might be, and he’s not shy about saying so. In this episode, he and Demetrios Brinkmann get real about the messy, over-engineered state of agent systems, why LLMs still struggle in the wild, and how enterprises keep tripping over their own data chaos. They unpack red-teaming, security headaches, and the uncomfortable truth that most “AI platforms” still don’t scale. If you want a sharp, no-fluff take on where agents are actually headed, this one’s worth a listen.
# AI Governance
# AI Agents
# AI infrastructure
Jure Leskovec & Demetrios Brinkmann · Nov 25th, 2025
Today’s foundation models excel at text and images—but they miss the relationships that define how the world works. In every enterprise, value emerges from connections: customers to products, suppliers to shipments, molecules to targets. This talk introduces Relational Foundation Models (RFMs)—a new class of models that reason over interactions, not just data points. Drawing on advances in graph neural networks and large-scale ML systems, I’ll show how RFMs capture structure, enable richer reasoning, and deliver measurable business impact. Audience will learn where relational modeling drives the biggest wins, how to build the data backbone for it, and how to operationalize these models responsibly and at scale.
# Structured Data
# Relational Deep Learning
# Enterprise AI