Browse Lessons

Real-Time Processing with Apache Flink, Kafka, and Pinot //Jacob Tsafatinos // Coffee Sessions #97

FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96

Uber’s Real-Time Ad Processing System // System Design Review

MLOps EngineeringLabs // Niels Bantilan and Haytham Abuelfutuh // MLOps Meetup #98

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

Modern Data Science with Vaex // Maarten Breddels and Jovan Veljanoski // MLOps Meetup #97

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 // Joseph Haaga // Coffee Sessions #91

Bringing Audio ML Models into Production // @ValerioVelardoTheSoundofAI //Coffee Sessions #90

A Journey in Scaling ML // Gabriel Straub // MLOps Coffee Sessions #89

ML Platform Tradeoffs and Wondering Why to Use Them // Javier Mansilla // MLOps Coffee Sessions #88

Building a Modern Data Analytics Stack // Jeff Katz // MLOps Meetup #96

Don't Listen Unless You Are Going to Do ML in Production // Kyle Morris // MLOps Coffee Sessions #87

Applications of Data Science // Connie Yang // MLOps Meetup #95

Building ML/Data Platform on Top of Kubernetes // Julien Bisconti // MLOps Coffee Sessions #86

Machine Learning Engineering in Action // Ben Wilson // Reading Group #5

MLOps at Stripe // Emmanuel Ameisen // MLOps Coffee Sessions #85

Orchestrating Machine Learning Workflows with Prefect // Kevin Kho // MLOps Meetup #94

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 // 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

Building an Open Source MLOps Stack with ZenML // Hamza Tahir // MLOps Workshop Meetup # 92

Calibration for ML at Etsy - apply() special // Erica Greene and Seoyoon Park // Coffee Sessions #78

Data Mesh - Data Quality Control Mechanism for MLOps? // Scott Hirleman // MLOps Coffee Sessions #77

MLOps Song

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

Federated Learning: Machine Learning on the Edge // Varun Kumar Khare // Reading group #3

Just Build It! Tips for Making ML Engineering and MLOps Real // Andy McMahon // MLOps Meetup #91

On Structuring an ML Platform 1 Pizza Team //Breno Costa & Matheus Frata //MLOps Coffee Sessions #73

Integration Testing That Never Works // Ewan Nicolson // MLOps Meetup #75 short clip

Setting up an ML Platform on GCP: Lessons Learned // Mefta Sadat // MLOps #71

2022 Predictions for MLOps and the Industry // Reah Miyara // MLOps Coffee Sessions #70

Self-Assembling Machine Learning Environment (SAME) // David Aronchick // Meetup #73 short clip

Building 12-Factor Data Apps with Kedro // Ivan Danov // MLOps Meetup #90

Building for Small Data Science Teams // James Lamb // MLOps Coffee Sessions #69

Modern ML Stack is a Lie

What is Kubeflow? // Aniruddha Choudhury // Meetup #72 short clip

ML Drift - How to Identify Issues Before They Become Problems // Amy Hodler // MLOps Meetup #89

Wikimedia MLOps // Chris Albon // MLOps Coffee Sessions #68