TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.

I've written a bit about automated machine learning in the past. I won't recap any of the introductory material what I have already covered, but if interested in running down the main points, as well as a foray into the practical, feel free to peruse these articles before moving on. Semantically, the training of a machine learning model, while the result of these automated steps, is incidental to the automated machine learning process, while automated steps such as model evaluation and model selection are ancillary to the core. This is all well and good, theoretically speaking. But what if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline with respect to the 2 overarching tasks outlined above?