From zero to MLOps: An open source stack to fight spaghetti ML

Abstract

The ecosystem of MLOps tools and platforms keeps growing by the year and it’s difficult to stay up to date. Luckily our industry is now more mature and certain good practices are already well established, but it’s still difficult for newcomers to navigate the complexity of production machine learning systems.

What are the minimal pieces that you need to build your MLOps stack? Is there a way to avoid vendor lock-in by stitching open source components together? What are the pros and cons of this approach? What have we learned since 2015, when the seminal Google paper “Hidden Technical Debt in Machine Learning Systems” appeared?

Full outline and instructions at https://github.com/astrojuanlu/workshop-from-zero-to-mlops/