This is the first in a series of posts on TensorFlow Extended. In this post I’ll set the stage without diving into any code yet, but bare with me. In subsequent posts, I’ll try to steer clear from the typical “getting started” content (the official docs do a fantastic job at that) and instead dive into some real-world use cases that require going beyond the built-in components and standard approach. I will assume some familiarity with basic machine learning and data science terminology.
ML engineer / data scientist @ g-company