MLOPS 101: Tips, Tricks and Best Practices
summary
A practical checklist replaces hype with actions scoping asks the questions that kill 80% of ML projects at scoping by forcing clear success metrics and data access windows scoping targets getting a working model in production within weeks not months the talk shows concrete tooling and patterns for pipeline, deployment and monitoring including metaflow + AWS Batch decorators to run notebook steps in cloud, shadow mode then A/B promotion, dashboards for data drift explainability and a recommendation to start teams with a data engineer to move data reliably into production
AI-written summary · about
chapters (13)
- 0:00MLOps lifecycle overview
- 1:11What is MLOps
- 2:55MLOps stages
- 6:48ML project questions
- 9:58Data viability questions
- 14:11Modeling best practices
- 17:20Deployment strategies
- 19:28Monitoring in MLOps
- 21:56DevOps vs MLOps
- 23:41MLOps team roles
- 26:29End-to-end tooling
- 28:13Metaflow example
- 30:20Q&A
related talks
43mNavigating the MLOps Journey: Key Considerations for Successful Implementations
40mA Hitchhiker Guide to MLOps
13mMLOps: From AI Research to Production-Ready Systems
24mA Hitchhiker's Guide to the MLOps Experience
26mMLOps for the rest of us- A poor man's guide to putting models in production
22mWhy you need to think about MLOps at the beginning of your project
13mMLOps at Scale
41mTHE STATE OF MLOPS - MACHINE LEARNING IN PRODUCTION AT ENTERPRISE SCALE