Reading Group: Practical MLOps

MLOpsCommunity's reading group for Practical MLOps by Noah Gift and Alfredo Deza

Book Description

Book description

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.

Code: https://github.com/noahgift/github-actions-demo/tree/main/.github/workflows

Format

This is an async first reading club:

• Don't push yourself to read the entire book, select a few chapters (5 or 6) that you are really interested about
• There's no hurry, but working through a chapter per week is a good pace
• On the discussions tab, you will find a discussion for each chapter
• Each chapter discussion serves as free forum: add questions, thoughts, ideas
• Once you have completed the chapter, leave a comment with your learnings
• If you want to share code, feel free to create a repository and add share the files you want to discuss to the chapter discussion
• Sync conversations are still useful and fun! Ping us on #reading-group to see if we can schedule one!

Chapter Discussions

1. Introduction to MLOps
2. MLOps Foundations
3. MLOps for Containers and Edge Devices
4. Continuous Delivery for Machine Learning Models
5. AutoML and KaizenML
6. Monitoring and Logging
7. MLOps for AWS
8. MLOps for Azure
9. MLOps for GCP
10. Machine Learning Interoperability
11. Building MLOps Command Line Tools and Microservices
12. Machine Learning Engineering and MLOps Case Studies

Questions? Reach us out at the #reading-group channel on MLops Community, or open an issue here