Beginner Lessons

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

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

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

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

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

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

Modern ML Stack is a Lie

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

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

The ML Test Score // Skylar Payne // MLOps Reading Group #2

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 // MLOps Coffee Sessions #65

Impact of SWE in ML Projects // Laszlo Sragner and Tim Blazina // MLOps Reading Group #1

The Future of AI and ML in Process Automation // Slater Victoroff // MLOps Coffee Sessions #64

Cybersecurity Threats // Monika Venčkauskaitė // Meetup #70 short clip

Data-Centric AI Means Centralizing Training Data // Alberto Rizzoli // MLOps Meetup #85

I Don't Like Jupyter Notebooks // Joel Grus // Coffee Sessions #62

MLOps at Volvo Cars // Leonard Aukea // MLOps Meetup #84

War Stories Prior to Having AI Development Process // Kseniia Melnikova // Meetup#65 short clip

ML Tests // Svet Penkov // Coffee Sessions #61

MLOps at Pinterest - System Design Review Lesson

KServe (Kubeflow KFServing) Live Coding Session // Theofilos Papapanagiotou // MLOps Meetup #83