Pragmatic ML development with scikit-learn and TensorFlow using Google ML Engine

Who doesn’t love jumping into a fresh Jupyter notebook at the start of a new machine learning project? However, the romance can soon fade if you have multiple people creating new notebooks all over the place with different approaches to the same problem. Zack Akil shares pragmatic techniques and useful tools that can help you avoid common pitfalls when building ML, including tools for notebook collaboration and version control that will help prevent you and your teammates from stepping on each others’ toes as well as an iterative ML model development approach that will prevent your project from stagnating. You’ll also learn how to quickly productionize your models with machine learning frameworks like TensorFlow and scikit-learn that are trying to solve the same problem using Google ML Engine.