All Episodes
MLOps.community — 549 episodes
Why Cost Per Million Tokens Is A Useless KPI?
The Five-Layer Cake Approach to Scaling AI Without Wasting Money
The Winchester Mystery House Problem in AI Development
How Predictive Analytics Stops Budget Overruns Before They Happen?
How To Delegate To An Agent Like You Would An Employee?
Why Your AI Bill Will Double Before It Gets Better
MCP Goes Stateless
AI Hype vs. Real Value
The Creator of FastMCP Explains the Future of MCP
What Happens When Every Developer Has 20 AI Agents?
AI Agents Should Be Treated Like Hackers
Developers May Stop Depending on Libraries
10 Cities. 4 Countries. One Unexpected MCP Lesson.
The Next Programming Language Is English
Omnigent: Composition, Control, and Collaboration for AI Agents
The Current State of Agentic Retrieval - Qdrant Roundtable
AI Agents in Healthcare?
Coding Agents Are Secretly General Agents
The Dark Side of MCP Servers
Sandboxing, Agent Harnesses, and Agent Teamwork
Zipline Roundtable episode: Building Real-Time ML Systems with Zipline + Chronon
MCP Servers Are Becoming the UI for AI Agents
Agents & the $40M Bet on Multiplayer AI
From Single-Player to Multi-Player: Operating AI Agents at Scale
The Control-vs-Magic Spectrum Building Agents
Logs Are All You Need: Rethinking Observability with AI Agents
AI Is Fast. AI Projects Are Slow. Let's Fix That.
Architecting Modern AI Systems: Platforms, Agents, and Integration
[Special Announcement] MLOps Community Linux Foundation
Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce
Autonomous Agents at Work: From OpenClaw Hype to Enterprise Reality
Agents are Just While Loops
The Latency Goldilocks Zone Explained
Building MCP Before MCP Existed: Inside Despegar's Sofia Agent
Voice Agent Use Cases
The Creator of Superpowers: Why Real Agentic Engineering Beats Vibe Coding
It's 2026, and We're Still Talking Evals
Why Agents are Driving Software Development to the Cloud
The Modern Software Engineer
We Cut LLM Latency by 70% in Production
Getting Humans Out of the Way: How to Work with Teams of Agents
Fixing GPU Starvation in Large-Scale Distributed Training
Spec Driven Development, Workflows, and the Recent Coding Agent Conference
Operationalizing AI Agents: From Experimentation to Production // Databricks Roundtable
arrowspace: Vector Spaces and Graph Wiring
Agentic Marketplace
Durable Execution and Modern Distributed Systems
Performance Optimization and Software/Hardware Co-design across PyTorch, CUDA, and NVIDIA GPUs
Serving LLMs in Production: Performance, Cost & Scale // CAST AI Roundtable
The Future of Information Retrieval: From Dense Vectors to Cognitive Search
Rethinking Notebooks Powered by AI
Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale
Physical AI: Teaching Machines to Understand the Real World
Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth
Cracking the Black Box: Real-Time Neuron Monitoring & Causality Traces
A Playground for AI/ML Engineers
How Universal Resource Management Transforms AI Infrastructure Economics
Conversation with the MLflow Maintainers
Leadership on AI
Computers that Think and Take Actions for You
Real time features, AI search, Agentic similarities
Tool definitions are the new Prompt Engineering
The Future of AI Agents is Sandboxed
Context engineering 2.0, Agents + Structured Data, and the Redis Context Engine
Does AgenticRAG Really Work?
How Sierra AI Does Context Engineering
Overcoming Challenges in AI Agent Deployment: The Sweet Spot for Governance and Security // Spencer Reagan // #349
Hardening Agents for E-commerce Scale: From RL Alignment to Reliability // Panel 2
Building Cursor: A Fireside Chat with VP Solutions Ricky Doar
Relational Foundation Models: Unlocking the Next Frontier of Enterprise AI // Jure Leskovec // #348
Context Engineering, Context Rot, & Agentic Search with the CEO of Chroma, Jeff Huber
Reliable Voice Agents
The Future of AI Operations: Insights from PwC AI Managed Services
GPU Uptime with VAST Data CTO
The Evolution of AI in Cyber Security // Jeff Schwartzentruber // #344
Thousands of Fine-Tuned Models
The Semantic Layer and AI Agents // David Jayatillake // #343
Building Claude Code: Origin, Story, Product Iterations, & What's Next // Siddharth Bidasaria // #342
Building an Agentic AI Memory Framework
LLMs at Scale: Infrastructure That Keeps AI Safe, Smart & Affordable // Marco Palladino// # 341
Best AI Hackathon Project Ever? [Bite Size Episode]
On-Device AI Agents in Production: Privacy, Performance, and Scale // Varun Khare & Neeraj Poddar // #340
Are Evals Dead?
The DuckLake Lakehouse Format // Hannes Mühleisen // #339
How LiveKit Became An AI Company By Accident
Economics of Building Data Centers, GPU Clouds, Sovereign AI
Trust at Scale: Security and Governance for Open Source Models // Hudson Buzby // #338
LLM Search, UI/UX challenges, Context Engineering and the 80/20 of Eval
The Era of AI Agents in Marketing // Joel Horwitz // #337
Distilling 200+ Hours of NeurIPS: What’s Next for AI // Nikolaos Vasiloglou // #336
Building Coding Agents: Design Decisions, Prompting Tricks, GUI Anti-patterns
A Candid Conversation with the CEO of Stack Overflow
Knowledge is Eventually Consistent // Devin Stein // #335
LinkedIn Recommender System Predictive ML vs LLMs
GPU Considerations, Labeling Privacy, Rapid Fine Tuning, and the Role of Private Eval Pipelines to Benchmark New Models
The Hidden Bottlenecks Slowing Down AI Agents
9 Commandments for Building AI Agents
Enterprise AI Adoption Challenges
Real-time Feature Generation at Lyft // Rakesh Kumar // #334
AI Agent Development Tradeoffs You NEED to Know
From the Legal Trenches to Tech // Nick Coleman // #332
The Rise of Sovereign AI and Global AI Innovation in a World of US Protectionism // Frank Meehan // MLOps Podcast #331
A New Way of Building with AI
Inside Uber’s AI Revolution - Everything about how they use AI/ML
The Missing Data Stack for Physical AI
AI Reliability, Spark, Observability, SLAs and Starting an AI Infra Company
Greg Kamradt: Benchmarking Intelligence | ARC Prize
Bridging the Gap Between AI and Business Data // Deepti Srivastava // #325
The Creator of FastAPI’s Next Chapter // Sebastián Ramírez // #324
Everything Hard About Building AI Agents Today
Tricks to Fine Tuning // Prithviraj Ammanabrolu // #318
Packaging MLOps Tech Neatly for Engineers and Non-engineers // Jukka Remes // #322
Hard Learned Lessons from Over a Decade in AI
Product Metrics are LLM Evals // Raza Habib CEO of Humanloop // #320
Getting AI Apps Past the Demo // Vaibhav Gupta // #319
Building Out GPU Clouds // Mohan Atreya // #317
A Candid Conversation Around MCP and A2A // Rahul Parundekar and Sam Partee // #316 SF Live
AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315
AI, Marketing, and Human Decision Making // Fausto Albers // #313
MLOps with Databricks // Maria Vechtomova // #314
Making AI Reliable is the Greatest Challenge of the 2020s // Alon Bochman // #312
Behavior Modeling, Secondary AI Effects, Bias Reduction & Synthetic Data // Devansh Devansh // #311
GraphBI: Expanding Analytics to All Data Through the Combination of GenAI, Graph, & Visual Analytics // Paco Nathan & Weidong Yang // #310
AI Data Engineers - Data Engineering After AI // Vikram Chennai // #309
I Am Once Again Asking "What is MLOps?" // Oleksandr Stasyk // #308
How Sama is Improving ML Models to Make AVs Safer // Duncan Curtis // #307
Agents of Innovation: AI-Powered Product Ideation with Synthetic Consumer Testing // Luca Fiaschi // #306
Real-Time Forecasting Faceoff: Time Series vs. DNNs // Josh Xi // #305
We're All Finetuning Incorrectly // Tanmay Chopra // #304
From Shiny to Strategic: The Maturation of AI Across Industries // David Cox // #303
Streaming Ecosystem Complexities and Cost Management // Rohit Agrawal // #302
Fraud Detection in the AI Era // Rafael Sandroni // #301
Beyond the Matrix: AI and the Future of Human Creativity
Efficient GPU infrastructure at LinkedIn // Animesh Singh // MLOps Podcast #299
Building Trust Through Technology: Responsible AI in Practice // Allegra Guinan // #298
Claude Plays Pokémon - A Conversation with the Creator // David Hershey // #297
From Rules to Reasoning Engines // George Mathew // #297
GenAI Traffic: Why API Infrastructure Must Evolve... Again // Erica Hughberg // #296
The Unbearable Lightness of Data // Rohit Krishnan // #295
Kubernetes, AI Gateways, and the Future of MLOps // Alexa Griffith // #294
Future of Software, Agents in the Enterprise, and Inception Stage Company Building // Eliot Durbin // #293
The Agent Exchange: Practitioner Insights
Talk to Your Data: The SQL Data Analyst
Getting to Grips with Web Agents
The Challenge with Voice Agents
The Agent Landscape - Lessons Learned Putting Agents Into Production
Evolving Workflow Orchestration // Alex Milowski // #291
Insights from Cleric: Building an Autonomous AI SRE // Willem Pienaar // #290
Robustness, Detectability, and Data Privacy in AI // Vinu Sankar Sadasivan // #289
AI & Aliens: New Eyes on Ancient Questions // Richard Cloete // #288
Real LLM Success Stories: How They Actually Work // Alex Strick van Linschoten // #287
Navigating Machine Learning Careers: Insights from Meta to Consulting // Ilya Reznik // #286
Collective Memory for AI on Decentralized Knowledge Graph // Tomaž Levak // #285
Efficient Deployment of Models at the Edge // Krishna Sridhar // #284
Real World AI Agent Stories // Zach Wallace // #283
Machine Learning, AI Agents, and Autonomy // Egor Kraev // #282
Re-Platforming Your Tech Stack // Michelle Marie Conway & Andrew Baker // #281
Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280
Unleashing Unconstrained News Knowledge Graphs to Combat Misinformation // Robert Caulk // #279
LLM Distillation and Compression // Guanhua Wang // #278
AI's Next Frontier // Aditya Naganath // #277
PyTorch for Control Systems and Decision Making // Vincent Moens // #276
AI-Driven Code: Navigating Due Diligence & Transparency in MLOps // Matt van Itallie // #275
PyTorch's Combined Effort in Large Model Optimization // Michael Gschwind // #274
LLMs to agents: The Beauty & Perils of Investing in GenAI // VC Panel // Agents in Production
We Can All Be AI Engineers and We Can Do It with Open Source Models // Luke Marsden // #273
Exploring AI Agents: Voice, Visuals, and Versatility // Panel // Agents in Production
The Impact of UX Research in the AI Space // Lauren Kaplan // #272
EU AI Act - Navigating New Legislation // Petar Tsankov // MLOps Podcast #271
Boosting LLM/RAG Workflows & Scheduling w/ Composable Memory and Checkpointing // Bernie Wu // #270
How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269
Exploring the Impact of Agentic Workflows // Raj Rikhy // #268
The Only Constant is (Data) Change // Panel // DE4AI
The AI Dream Team: Strategies for ML Recruitment and Growth // Jelmer Borst and Daniela Solis // #267
Making Your Company LLM-native // Francisco Ingham // #266
Unpacking 3 Types of Feature Stores // Simba Khadder // #265
Reinvent Yourself and Be Curious // Stefano Bosisio // #264
Global Feature Store // Gottam Sai Bharath & Cole Bailey // #263
RAG Quality Starts with Data Quality // Adam Kamor // #262
Who's MLOps for Anyway? // Jonathan Rioux // #261
Alignment is Real // Shiva Bhattacharjee // #260
Ax a New Way to Build Complex Workflows with LLMs // Vikram Rangnekar // #259
Building in Production Human-centred GenAI Solutions // Mohamed Abusaid & Mara Pometti// #177
Visualize - Bringing Structure to Unstructured Data // Markus Stoll // #258
AI Testing Highlights // Special MLOps Podcast Episode
MLSecOps is Fundamental to Robust AISPM // Sean Morgan // #257
MLOps for GenAI Applications // Harcharan Kabbay // #256
BigQuery Feature Store // Nicolas Mauti // #255
Design and Development Principles for LLMOps // Andy McMahon // #254
Data Quality = Quality AI // AIQCON Panel
The Variational Book // Yuri Plotkin // #253
Vision and Strategies for Attracting & Driving AI Talents in High Growth // Panel // AIQCON
Red Teaming LLMs // Ron Heichman // #252
Balancing Speed and Safety // Panel // AIQCON
Reliable LLM Products, Fueled by Feedback // Chinar Movsisyan // #251
A Blueprint for Scalable & Reliable Enterprise AI/ML Systems // Panel // AIQCON
AI Operations Without Fundamental Engineering Discipline // Nikhil Suresh // #250
AI in Healthcare // Eric Landry // #249
Evaluating the Effectiveness of Large Language Models: Challenges and Insights // Aniket Singh // #248
Extending AI: From Industry to Innovation // Sophia Rowland & David Weik // #247
Detecting Harmful Content at Scale // Matar Haller // #246
All Data Scientists Should Learn Software Engineering Principles // Catherine Nelson // #245
Meta GenAI Infra Blog Review // Special MLOps Podcast
AI Agents for Consumers // Shaun Wei // #244
ML and AI as Distinct Control Systems in Heavy Industrial Settings // Richard Howes // #243
Accelerating Multimodal AI // Ethan Rosenthal // #242
Navigating the AI Frontier: The Power of Synthetic Data and Agent Evaluations in LLM Development // Boris Selitser // #241
How to Build Production-Ready AI Models for Manufacturing // [Exclusive] LatticeFlow Roundtable
From Robotics to Recommender Systems // Miguel Fierro // #240
Uber's Michelangelo: Strategic AI Overhaul and Impact // #239
AWS Tranium and Inferentia // Kamran Khan and Matthew McClean // #238
Build Reliable Systems with Chaos Engineering // Benjamin Wilms // #237
Managing Small Knowledge Graphs for Multi-agent Systems // Tom Smoker // #236
Just when we Started to Solve Software Docs, AI Blew Everything Up // Dave Nunez // #235
Open Standards Make MLOps Easier and Silos Harder // Cody Peterson // #234
Retrieval Augmented Generation
RecSys at Spotify // Sanket Gupta // #232
From A Coding Startup to AI Development in the Enterprise // Ryan Carson // #231
FedML Nexus AI: Your Generative AI Platform at Scale // Salman Avestimehr // #230
What is AI Quality? // Mohamed Elgendy // #228
Handling Multi-Terabyte LLM Checkpoints // Simon Karasik // #228
Leading Enterprise Data Teams // Sol Rashidi // #227
The Rise of Modern Data Management // Chad Sanderson // #226
Beyond AGI, Can AI Help Save the Planet? // Patrick Beukema // #225
GenAI in Production - Challenges and Trends // Verena Weber // #224
Introducing DBRX: The Future of Language Models // [Exclusive] Databricks Roundtable
From MVP to Production // AI in Production Conference
Data Engineering in the Federal Sector // Shane Morris // #223
What Business Stakeholders Want to See from the ML Teams // Peter Guagenti // #222
MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases // Amritha Arun Babu & Abhik Choudhury // #221
4 Years of the MLOps Community // Demetrios Brinkmann // #220
The Art and Science of Training LLMs // Bandish Shah and Davis Blalock // #219
Security and Privacy // Day 2 Panel 1 // AI in Production Conference
[Exclusive] Zilliz Roundtable // Why Purpose-built Vector Databases Matter for Your Use Case
A Decade of AI Safety and Trust // Petar Tsankov // MLOps Podcast #218
The Real E2E RAG Stack // Sam Bean, Rewind AI // #217
Managing Data for Effective GenAI Application // Anu Arora and Anass Bensrhir // #215
Becoming an AI Evangelist // Alex Volkov // #215
LLM Use Cases in Production // AI in Production Conference // Panel 1
Information Retrieval & Relevance // Daniel Svonava // #214
Evaluating and Integrating ML Models // Morgan McGuire and Anish Shah // #213
Data Governance and AI // Alexandra Diem // #212
Ads Ranking Evolution at Pinterest // Aayush Mudgal // #211
LLM Evaluation with Arize AI's Aparna Dhinakaran // #210
Powering MLOps: The Story of Tecton's Rift // Matt Bleifer & Mike Eastham // #209
[Exclusive] QuantumBlack Round-table // Gen AI Buy vs Build, Commercial vs Open Source
Micro Graph Transformer Powering Small Language Models // Jon Cooke // #208
How Data Platforms Affect ML & AI // Jake Watson // #207
RAG Has Been Oversimplified // Yujian Tang // #206
The Myth of AI Breakthroughs // Jonathan Frankle // #205
MLOps at the Crossroads // Patrick Barker & Farhood Etaati // #204
Pioneering AI Models for Regional Languages // Aleksa Gordić // #203
Small Data, Big Impact: The Story Behind DuckDB // Hannes Mühleisen & Jordan Tigani // #202
Language, Graphs, and AI in Industry // Paco Nathan // #201
Founding, Funding, and the Future of MLOps // Mihail Eric // #200
Challenges Operationalizing ML (And Some Solutions) // Nathan Ryan Frank // #199
Inferring Creativity // Nick Hasty // #198
The Role of Infrastructure in ML // Niels Bantilan // #197
LLMs in Focus: From One-Size Fits All to Verticalized Solutions // Venky Ganti & Laurel Orr // #196
[Exclusive] Weights & Biases Round-table // Model Management in a Regulated Environment
Building the Future of AI in Software Development // Varun Mohan // #195
AI in Education Fireside Chat // LLMs in Production Conference 3
[Exclusive] Tecton Round-table // Get your ML Application Into Production
DSPy: Transforming Language Model Calls into Smart Pipelines // Omar Khattab // #194
Fireside Chat with LLM Startups // LLMs in Production Conference 3
LLMs in Biomaterials Production // Pierre Salvy // #193
Product Engineering for LLMs // LLMs in Production Conference Part III // Panel 2
Enterprises Using MLOps, the Changing LLM Landscape, MLOps Pipelines // Chris Van Pelt // #192
Building Defensible AI Apps // Gregory Kamradt // #191
Guarding LLM and NLP APIs: A Trailblazing Odyssey for Enhanced Security // Ads Dawson // #190
Designing for Forward Compatibility in Gen AI // Rohit Agarwal // #189
Impact of LLMs on the Tech Stack and Product Development // Anand Das // #188
Building Effective Products with GenAI // Faizaan Charania // #187
The Future of Feature Stores and Platforms // Mike Del Balso & Josh Wills // # 186
Lessons on Data Science Leadership // Luigi Patruno // #185
Data Platforms in MLOps: Translating Business Goals into Product Decisions // Richa Sachdev // #184
MLOps vs ML Orchestration // Ketan Umare // #183
MLOps@GetYourGuide // Jean Machado, Meghana Satish, Olivia Houghton, Theodore Meynard// #182
The Centralization of Power in AI // Kyle Harrison // # 181
Adventures in Building CLIP & Other (Largeish) LMs // Sachin Abeywardana // #180
All About Evaluating LLM Applications // Shahul Es // #179
Building an ML Platform: Insights, Community, and Advocacy // Stephen Batifol // #178
Collaboration and Strategy // Vin Vashishta // #176
Ux of an LLM User Panel // LLMs in Production Conference Part II
From Virtualization to AI Integration // Lamia Youseff // # 175
LLM on K8s Panel // LLMs in Conference in Production Conference Part II
Harnessing MLOps in Finance // Michelle Marie Conway // #174
MLOps vs. LLMOps Panel // LLMs in Conference in Production Conference Part II
Building Cody, an Open Source AI Coding Assistant // Beyang Liu // #173
Evaluation Panel // Large Language Models in Production Conference Part II
FrugalGPT: Better Quality and Lower Cost for LLM Applications // Lingjiao Chen // #172
Building LLM Products Panel // LLMs in Production Conference Part II
Using Large Language Models at AngelList // Thibaut Labarre // #171
All the Hard Stuff with LLMs in Product Development // Phillip Carter // #170
MLOps at the Age of Generative AI // Barak Turovsky // #169
Experiment Tracking in the Age of LLMs // Piotr Niedźwiedź // #168
Treating Prompt Engineering More Like Code // Maxime Beauchemin // #167
Eliminating Garbage In/Garbage Out for Analytics and ML // Roy Hasson & Santona Tuli // #166
Python Power: How Daft Embeds Models and Revolutionizes Data Processing // Sammy Sidhu // #165
Open Source and Fast Decision Making // Rob Hirschfeld // #164
Democratizing AI // Yujian Tang // #163
From Arduinos to LLMs: Exploring the Spectrum of ML // Soham Chatterjee // #162
The Long Tail of ML Deployment // Tuhin Srivastava // #161
Clean Code for Data Scientists // Matt Sharp // # 160
Why is MLOps Hard in an Enterprise? // Maria Vechtomova & Basak Eskili // #159
Large Language Models at Cohere // Nils Reimers // #158
Data Privacy and Security // LLMs in Production Conference Panel Discussion
MLOps Build or Buy, Startup vs. Enterprise? // Aaron Maurer & Katrina Ni # 157
Cost/Performance Optimization with LLMs [Panel]
Machine Learning Education at Uber // Melissa Barr & Michael Mui // MLOps Podcast #156
The Birth and Growth of Spark: An Open Source Success Story // Matei Zaharia // MLOps Podcast #155
ML Scalability Challenges // Waleed Kadous // MLOps Podcast # 154
[EXCLUSIVE EPISODE!] LLM Key Results
Multilingual Programming and a Project Structure to Enable It // Rodolfo Núñez // MLOps Podcast #153
[Bonus Episode] Practical AI x MLOps // Demetrios Brinkmann, Mihail Eric, Daniel Whitenack and Chris Benson
How A Manager Became a Believer in DevOps for Machine Learning // Keith Trnka // MLOps Podcast #152
ML in Production: A DS from Ubisoft Perspective // Jean-Michel Daignan // MLOps Podcast #151
Large Language Models in Production Round-table Conversation
The Future of Search in the Era of Large Language Models // Saahil Jain // MLOps Podcast #150
The Challenges of Deploying (many!) ML Models // Jason McCampbell // MLOps Podcast #149
Intelligence & MLOps // Karl Fezer // MLOps Podcast # 148
The Rise of Serverless Databases // Alex DeBrie // MLOps Podcast #147
The Ops in MLOps - Process and People // Shalabh Chaudri // MLOps Podcast #146
Griffin, ML Platform at Instacart // Sahil Khanna // MLOps Podcast #145
Non-traditional Career Paths in MLOps // Matthew Dombrowski // MLOps Podcast #144
Investing in the Next Generation of AI & ML // Jill Chase & Manmeet Gujral // MLOps Podcast #143
Approaches to Fairness and XAI // Murtuza Shergadwala // MLOps Podcast #142
Airflow Sucks for MLOps // Stephen Bailey // MLOps Podcast #141
Updated The Evolution of ML Infrastructure // Sakib Dadi // MLOps Podcast #140
Foundational Models are the Future but... with Alex Ratner CEO of Snorkel AI // MLOps Podcast #139
Explainability in the MLOps Cycle // Dattaraj Rao // MLOps Podcast #138
Machine Learning Operations — What is it and Why Do We Need It? // Niklas Kühl // MLOps Podcast #137
Systems Engineer Navigating the World of ML // Andrew Dye // MLOps Podcast #136
"Real-Time" ML: Features and Inference // Sasha Ovsankin and Rupesh Gupta // MLOps Podcast #135
Building Threat Detection Systems: An MLE's Perspective // Jeremy Jordan // MLOps Podcast #134
Real-time Machine Learning with Chip Huyen // MLOps Coffee Sessions #133
What is Data / ML Like on League? // Ian Schweer // MLOps Coffee Sessions #132
Let's Continue Bundling into the Database // Ethan Rosenthal // MLOps Coffee Sessions #131
MLOps for Ad Platforms // Andrew Yates // MLOps Coffee Sessions #130
Voice and Language Tech // Catherin Breslin // Coffee Sessions #129
Managing Machine Learning Projects // Simon Thompson // MLOps Coffee Sessions #128
Reliable ML // Niall Murphy & Todd Underwood // Coffee Sessions #127
ML Unicorn Start-up Investor Tells-IT-All // George Mathew // MLOps Coffee Sessions #126
Databricks Model Serving V2 // Rafael Pierre // Coffee Sessions #125
Monitoring Unstructured Data // Aparna Dhinakaran & Jason Lopatecki // Lightning Sessions #2
Trustworthy Machine Learning // Kush Varshney // Coffee Sessions #124
RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day // Gleb Abroskin // MLOps Coffee Sessions #123
Scaling Similarity Learning at Digits // Hannes Hapke // Coffee Sessions #122
Bringing DevOps Agility to ML// Luis Ceze // Coffee Sessions #121
Feathr: LinkedIn's High-performance Feature Store // David Stein // Coffee Sessions #120
MLOps at DoorDash // Hien Luu and DoorDash Leads // Coffee Sessions #119
ML Platforms, Where to Start? // Olalekan Elesin // Coffee Sessions #118
Data Engineering for ML // Chad Sanderson // Coffee Sessions #117
Scaling Machine Learning with Data Mesh // Shawn Kyzer // Coffee Sessions #116
How Hera is an Enabler of MLOps Integrations // Flaviu Vadan // Coffee Sessions #115
Product Enrichment and Recommender Systems // Marc Lindner and Amr Mashlah // Coffee Sessions #114
Building Better Data Teams // Leanne Fitzpatrick // Coffee Sessions #113
MLX: Opinionated ML Pipelines in MLflow // Xiangrui Meng // Coffee Sessions #112
More than a Cache: Turning Redis into a Composable, ML Data Platform // Samuel Partee // Coffee Sessions #111
Just Fetch the Data and then... // David Bayliss // Coffee Sessions #110
Why You Need More Than Airflow // Ketan Umare // Coffee Sessions #109
ML Flow vs Kubeflow 2022 // Byron Allen // Coffee Sessions #108
Why and When to Use Kubeflow for MLOps // Ryan Russon // Coffee Sessions #107
Building a Culture of Experimentation to Speed Up Data-Driven Value // Delina Ivanova // MLOps Coffee Sessions #106
Cleanlab: Labeled Datasets that Correct Themselves Automatically // Curtis Northcutt // MLOps Coffee Sessions #105
MLOps + BI? // Maxime Beauchemin // MLOps Coffee Sessions #104
Making MLFlow // Lead MLFlow Maintainer Corey Zumar // MLOps Coffee Sessions #103
Fixing Your ML Data Blind Spots // Yash Sheth // MLOps Coffee Sessions #102
Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team // Piero Molino // MLOps Coffee Sessions #101
Scaling Real-time Machine Learning at Chime // Peeyush Agarwal // Lightning Sessions #1
MLOps Critiques // Matthijs Brouns // MLOps Coffee Sessions #100
CPU vs GPU // Ronen Dar & Gijsbert Janssen van Doorn // MLOps Coffee Sessions #99
Racing the Playhead: Real-time Model Inference in a Video Streaming Environment // Brannon Dorsey // Coffee Sessions #98
Real-Time Exactly-Once Event Processing with Apache Flink, Kafka, and Pinot //Jacob Tsafatinos // MLOps Coffee Sessions #97
FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96
MLOps as Tool to Shape Team and Culture // Ciro Greco // MLOps Coffee Sessions #95
Traversing the Data Maturity Spectrum: A Startup Perspective // Mark Freeman // Coffee Sessions #94
Model Monitoring in Practice: Top Trends // Krishnaram Kenthapadi // MLOps Coffee Sessions #93
Building the World's First Data Engineering Conference // Pete Soderling // MLOps Coffee Sessions #92
The Shipyard: Lessons Learned While Building an ML Platform / Automating Adherence // Joseph Haaga // Coffee Sessions #91
Bringing Audio ML Models into Production // Valerio Velardo // MLOps Coffee Sessions #90
A Journey in Scaling AI // Gabriel Straub // MLOps Coffee Sessions #89
ML Platform Tradeoffs and Wondering Why to Use Them // Javier Mansilla // MLOps Coffee Sessions #88
Don't Listen Unless You Are Going to Do ML in Production // Kyle Morris // MLOps Coffee Sessions #87
Building ML/Data Platform on Top of Kubernetes // Julien Bisconti // MLOps Coffee Sessions #86
Continuous Deployment of Critical ML Applications // Emmanuel Ameisen // MLOps Coffee Sessions #85
Lessons from Studying FAANG ML Systems // Ernest Chan // MLOps Coffee Sessions #84
Better Use cases for Text Embeddings // Vincent Warmerdam // MLOps Coffee Sessions #83
Feature Stores at Shopify and Skyscanner // Matt Delacour and Mike Moran // Reading Group #4
Trustworthy Data for Machine Learning // Chad Sanderson // MLOps Meetup #93
Practitioners Guide to MLOps // Donna Schut and Christos Aniftos // Coffee Sessions #82
Investing in MLOps // Leigh Marie Braswell and Davis Treybig // MLOps Coffee Sessions #81
The Journey from Data Scientist to MLOps Engineer // Ale Solano // MLOps Coffee Sessions #80
Platform Thinking: A Lemonade Case Study // Orr Shilon // MLOps Coffee Sessions #79
Calibration for ML at Etsy - apply() special // Erica Greene and Seoyoon Park // MLOps Coffee Sessions #78
Data Mesh - The Data Quality Control Mechanism for MLOps? // Scott Hirleman // MLOps Coffee Sessions #77
Build a Culture of ML Testing and Model Quality // Mohamed Elgendy // MLOps Coffee Sessions #76
Towards Observability for ML Pipelines // Shreya Shankar // MLOps Coffee Sessions #75
Scaling Biotech // Jesse Johnson // MLOps Coffee Sessions #74
On Structuring an ML Platform 1 Pizza Team //Breno Costa & Matheus Frata //MLOps Coffee Sessions #73
2021 MLOps Year in Review // Vishnu Rachakonda and Demetrios Brinkmann // MLOps Coffee Sessions #72
Setting up an ML Platform on GCP: Lessons Learned // Mefta Sadat // MLOps Coffee Sessions #71
2022 Predictions for MLOps and the Industry // Reah Miyara // MLOps Coffee Sessions #70
Building for Small Data Science Teams // James Lamb // MLOps Coffee Sessions #69
Wikimedia MLOps // Chris Albon // Coffee Sessions #68
ML Stepping Stones: Challenges & Opportunities for Companies // John Crousse // Coffee Sessions #67
Machine Learning at Reasonable Scale // Jacopo Tagliabue // MLOps Coffee Sessions #66
The Future of Data Science Platforms is Accessibility // Skylar Payne // Coffee Session #65
Impact of SWE in ML Projects // Laszlo Sragner and Tim Blazina // MLOps Reading Group
The Future of AI and ML in Process Automation // Slater Victoroff // MLOps Coffee Sessions #64
PyTorch: Bridging AI Research and Production // Dmytro Dzhulgakov // Coffee Sessions #63
I Don't Like Jupyter Notebooks // Joel Grus // Coffee Sessions #62
ML Tests // Svet Penkov // Coffee Sessions #61
Linkedin Job Recommendations // Alexandre Patry // Coffee Sessions #60
Data Selection for Data-Centric AI: Data Quality Over Quantity // Cody Coleman // Coffee Sessions #59
10 Types of Features your Location ML Model is Missing // Anne Cocos // Coffee Sessions #58
The Future of ML and Data Platforms // Michael Del Balso - Erik Bernhardsson // Coffee Sessions #57
A Few Learnings from Building a Bootstrapped MLOps Services Startup //Soumanta Das// Coffee Sessions #56
Learning and Teaching MLOps Applications // Salwa Muhammad // MLOps Coffee Sessions #55
Machine Learning SRE // Niall Murphy // MLOps Coffee Sessions #54
MLOps Insights // David Aponte-Demetrios Brinkmann-Vishnu Rachakonda // MLOps Coffee Sessions #53
Vector Similarity Search at Scale // Dave Bergstein // MLOps Coffee Sessions #52
ML Security: Why should you care? // Sahbi Chaieb // MLOps Coffee Sessions #51
Creating MLOps Standards // Alex Chung and Srivathsan Canchi // MLOps Coffee Sessions #50
Aggressively Helpful Platform Teams // Stefan Krawczyk // MLOps Coffee Sessions #49
Tour of Upcoming Features on the Hugging Face Model Hub // Julien Chaumond // MLOps Coffee Sessions #48
Fast.ai, AutoML, and Software Engineering for ML: Jeremy Howard // Coffee Session #47
Learning from 150 Successful ML-enabled Products at Booking.com // Pablo Estevez // Coffee Sessions #46
Machine Learning in Cyber Security // Monika Venckauskaite // MLOps Meetup #70
Enterprise Security and Governance MLOps // Diego Oppenheimer // MLOps Coffee Sessions #45
Autonomy vs. Alignment: Scaling AI teams to deliver value // Grant Wright // MLOps Coffee Sessions #44
How Pinterest Powers Image Similarity // Shaji Chennan Kunnummel // System Design Reviews #1
Engineering MLOps // Emmanuel Raj // MLOps Meetup #69
Project/Product Management for MLOps // Korri Jones - Simarpal Khaira - Veselina Staneva // MLOps Meetup #68
Maturing Machine Learning in Enterprise // Kyle Gallatin // MLOps Coffee Sessions #43
Practical MLOps Part 2 // Alfredo Deza // MLOps Meetup #66
Common Mistakes in the ML Development Lifecycle // Kseniia Melnikova // MLOps Meetup #65
Model Performance Monitoring and Why You Need it Yesterday // Amit Paka // MLOps Coffee Sessions #42
CI/CD in MLOPS // Monmayuri Ray // MLOps Coffee Sessions #41
Operationalizing Machine Learning at Scale // Christopher Bergh // MLOps Meetup #64
Scaling AI in production // Srivatsan Srinivasan // MLOps Coffee Sessions #40
MLOps: A leader's perspective // Stephen Galsworthy // MLOps Coffee Sessions #39
Learnings from Live Coding: An MLOps Project on Twitch // Felipe Campos Penha // MLOps Meetup #63
Law of Diminishing Returns for Running AI Proof-of-Concepts // Oguzhan Gencoglu // MLOps Meetup #62
Organisational Challenges of MLOps // Adam Sroka // MLOps Coffee Sessions #38
From Idea to Production ML // Lex Beattie - Michael Munn - Mike Moran // MLOps Meetup #61
MLOps Memes // Ariel Biller // MLOps Coffee Sessions #37
Luigi in Production Part 2 // Luigi Patruno // MLOps Coffee Sessions #36
War Stories Productionising ML // Nick Masca // Coffee Session #35
Deploying Machine Learning Models at Scale in Cloud // Vishnu Prathish // MLOps Meetup #60
Machine Learning at Atlassian // Geoff Sims // Coffee Session#34
MLOps Community 1 Year Anniversary! // Demetrios Brinkmann, David Aponte & Vishnu Rachakonda // MLOps Meetup #59
MLOps Investments // Sarah Catanzaro // Coffee Session #33
Model Watching: Keeping Your Project in Production // Ben Wilson // MLOps Meetup #58
A Missing Link in the ML Infrastructure Stack // Josh Tobin // MLOps Meetup #57
The Godfather Of MLOps // D. Sculley // MLOps Coffee Sessions #32
Operationalizing Machine Learning at a Large Financial Institution // Daniel Stahl // MLOps Meetup #56
How to Avoid Suffering in Mlops/Data Engineering Role // Igor Lushchyk // MLOps Meetup #55
Product Management in Machine Learning // Laszlo Sragner // MLOps Meetup #54
MLOps Engineering Labs Recap // Part 2 // MLOps Coffee Sessions #31
How Explainable AI is Critical to Building Responsible AI // Krishna Gade MLOps // Meetup #53
MLOps Engineering Labs Recap // Part 1 // MLOps Coffee Sessions #30
'Git for Data' - Who, What, How and Why? // Luke Feeney - Gavin Mendel-Gleason // MLOps Meetup #52
Agile AI Ethics: Balancing Short Term Value with Long Term Ethical Outcomes // Pamela Jasper // MLOps Meetup #51
Culture and Architecture in MLOps // Jet Basrawi // MLOps Coffee Sessions #29
2 tools to get you 90% operational // Michael Del Balso - Willem Pienaar - David Aronchick // MLOps Meetup #50
Machine Learning Design Patterns for MLOps // Valliappa Lakshmanan // MLOps Meetup #49
Lessons Learned From Hosting the Machine Learning Engineered Podcast // Charlie You // MLOps Coffee Sessions #28
Practical MLOps // Noah Gift // MLOps Coffee Sessions #27
Serving ML Models at a High Scale with Low Latency // Manoj Agarwal // MLOps Meetup #48
When Machine Learning meets privacy - Episode 9
Machine Learning Feature Store Panel Discussion // MLOps Coffee Sessions #26
ProductizeML: Assisting Your Team to Better Build ML Products // Adrià Romero // MLOps Meetup #47
When Machine Learning meets privacy - Episode 8
Most Underrated MLOps Topics // Marian Ignev MLOps // Coffee Sessions #25
Real-time Feature Pipelines, A Personal History // Hendrik Brackmann // MLOps Meetup #46
Machine Learning Design Patterns // Sara Robinson // MLOps Coffee Sessions #24
SRE for ML Infra // Todd Underwood // MLOps Coffee Sessions #23
How To Move From Barely Doing BI to Doing AI // Joe Reis // MLOps Meetup #45
Deep in the heart of data // Carl Steinbach // MLOps Coffee Sessions #22
When machine learning meets privacy - Episode 7
When Machine Learning meets privacy - Episode 6
Human-centric ML Infrastructure: A Netflix Original // Savin Goyal // MLOps Meetup #44
A Conversation with Seattle Data Guy // Benjamin Rogojan // MLOps Coffee Sessions #21
Monzo Bank - An MLOps Case Study // Neal Lathia // MLOps Coffee Sessions #20
When Machine Learning meets privacy - Episode 5
When Machine Learning meets privacy - Episode 4
Introducing Data Downtime: From Firefighting to Winning // Barr Moses // MLOps Coffee Sessions #19
The Current MLOps Landscape // Nathan Benaich & Timothy Chen // MLOps Meetup #43
When Machine Learning meets privacy - Episode 3 with Charles Radclyffe
UN Global Platform // Mark Craddock // Co-Founder & CTO, Global Certification and Training Ltd // MLOps Meetup #42
When Machine Learning meets Data Privacy - Episode 2 with Cat Coode
When You Say Data Scientist Do You Mean Data Engineer? Lessons Learned From Start Up Life // Elizabeth Chabot
Metaflow: Supercharging Our Data Scientist Productivity // Ravi Kiran Chirravuri // MLOps Meetup #41
Luigi in Production // MLOps Coffee Sessions #18 // Luigi Patruno ML in Production
When Machine Learning meets Data Privacy
Analyzing the Google Paper on Continuous Delivery in ML // Part 4 // MLOps Coffee Sessions #17
Hands-on serving models using KFserving // Theofilos Papapanagiotou // Data Science Architect at Prosus // MLOps Meetup #40
Operationalize Open Source Models with SAS Open Model Manager // Ivan Nardini // Customer Engineer at SAS // MLOps Meetup #39
Machine in Production = Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16
MLOps + Machine Learning // James Sutton // MLOps Coffee Sessions #15
Scalable Python for Everyone, Everywhere // Matthew Rocklin // MLOps Meetup #38
MLOps Coffee Sessions #13 How to Choose the Right Machine Learning Tool: A Conversation // Jose Navarro and Mariya Davydova
MLOps Coffee Sessions #14 Conversation with the Creators of Dask // Hugo Bowne-Anderson and Matthew Rocklin
MLOps Coffee Sessions #12: Journey of Flyte at Lyft and Through Open-source // Ketan Umare
MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3
MLOps Meetup #36: Moving Deep Learning from Research to Prod Using DeterminedAI and Kubeflow // David Hershey, DeterminedAI
MLOps Coffee Sessions #10 Analyzing the Article “Continuous Delivery and Automation Pipelines in Machine Learning" // Part 2
MLOps Meetup #34: Streaming Machine Learning with Apache Kafka and Tiered Storage // Kai Waehner, Confluent
MLOps Meetup #33 Owned By Statistics: How Kubeflow & MLOps Can Help Secure Your ML Workloads // David Aronchick - Head of Open Source ML Strategy at Azure
MLOps Coffee Sessions #9 Analyzing the Article “Continuous Delivery and Automation Pipelines in Machine Learning “ // Part 1
MLOps Meetup #32 Building Say Less: An AI-Powered Summarization App // Yoav Zimmerman - Founder of Model Zoo
MLOps Coffee Sessions #8 // MLOps from the Perspective of an SRE // Neeran Gul
MLOps Meetup #31 // Creating Beautiful Ambient Music with Google Brain’s Music Transformer // Daniel Jeffries - Chief Technology Evangelist at Pachyderm
MLOps Coffee Sessions #7 // MLOps and DevOps - Parallels and Deviations // Featuring Damian Brady
MLOps Meetup #30 // Path to Production and Monetizing Machine Learning // Vin Vashishta - Data Scientist | Strategist | Speaker & Author
MLOps Meetup #29 // Scaling Machine Learning Capabilities in Large Organizations // Bertjan Broeksema & Axel Goblet
MLOps Coffee Sessions #6 // Continuous Integration for ML // Featuring Elle O'Brien
MLOps Coffee Sessions #5 // Airflow in MLOps // Featuring Simon Darr and Byron Allen
MLOps #28 Continuous Evaluation & Model Experimentation // Danny Ma - Founder & CEO at Sydney Data Science
MLOps Coffee Sessions #4: A Conversation Around Feature Stores with Venkata Pingali and Jim Dowling
MLOps #27 ML Observability // Aparna Dhinakaran - Chief Product Officer at Arize AI
MLOps Meetup #26 // How to Leverage ML Tooling Ecosystem // Mariya Davydova - Head of Product at Neu.ro
MLOps Coffee Sessions #3 MLOps: Isn't That Just DevOps? // Featuring Ryan Dawson
MLOps Meetup #25 // Python and Dask: Scaling the DataFrame // Dan Gerlanc - Founder of Enplus Advisors
MLOps Meetup #23 // Monitoring the ML Stack // Lina Weichbrodt
MLOps Meetup #24 // How to Become a Better Data Scientist: The Definite Guide // Alexey Grigorev
MLOps #22 Feature Stores: An Essential Part of the ML Stack to Build Great Data // Kevin Stumpf - Co-Founder & CTO at Tecton
MLOps Meetup #21 Deep Dive on Paperspace Tooling // Misha Kutsovsky - Senior ML Architect at Paperspace
MLOps Meetup #18 // Nubank - Running a Fintech on ML // Caique Lima and Cristiano Breuel
MLOps Meetup #19 // DataOps and Data Versioning in ML // Dmitry Petrov
MLOps Coffee Sessions #1: Serving Models with Kubeflow
MLOps Meetup #17 // The Challenges of ML Operations & How Hermione Helps Along the Way // Neylson Crepalde
MLOps Meetup #16 // Venture Capital and Machine Learning Startups with John Spindler
MLOps Meetup #15 Scaling Human-in-the-Loop Machine Learning with Robert Munro
MLOps #14 Kubeflow vs MLflow with Byron Allen
MLOps meetup #13 // Maximizing Job Opportunities as a Data Scientist on the Market With Anthony Kelly
MLOps meetup #12 // Why Data Scientists Should Know Data Engineering with Dan Sullivan
MLOps community meetup #11 // Machine Learning at Scale in Mercado Libre with Carlos de la Torre
MLOps.community meetup #9 with Charles Martin - 10 Years Deploying Machine Learning in the Enterprise: The Inside Scoop!
MLOps.community #10 - MLOps - The Blind Men and the Elephant with Saurav Chakravorty
MLOps.community meetup #8: Optimizing your ML workflow with Kubeflow 1.0 with Josh Bottum VP of Arrikto
MLOps meetup #7- Machine Learning and Open Banking with Alex Spanos of TrueLayer
MLOps.community #6 - Mid Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps.community #5 - High Stakes ML: Latent Conditions and Active Failures with Flavio Clesio
MLOps.community #4 - Building an ML platform @SurveyMonkey with Shubhi Jain
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
What Does Best in Class AI/ML Governance Look Like in Financial Services? // Charles Radclyffe // MLOps Meetup #2
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1