Designing Machine Learning Workflows in Python

Overview

Context

Deploying machine learning models in production seems easy with modern tools, but often ends in disappointment as the model performs worse in production than in development.

This Designing Machine Learning Workflows in Python course at Data Camp will give you four superpowers that will make you stand out from the data science crowd and build pipelines that stand the test of time: how to exhaustively tune every aspect of your model in development; how to make the best possible use of available domain expertise; how to monitor your model in performance and deal with any performance deterioration; and finally how to deal with poorly or scarcely labelled data.