Book Image

Machine Learning Automation with TPOT

By : Dario Radečić
Book Image

Machine Learning Automation with TPOT

By: Dario Radečić

Overview of this book

The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods. With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets. By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level.
Table of Contents (14 chapters)
1
Section 1: Introducing Machine Learning and the Idea of Automation
3
Section 2: TPOT – Practical Classification and Regression
8
Section 3: Advanced Examples and Neural Networks in TPOT

Making predictions in a GUI environment

Welcome to the last section of the book. This section will tie our simple web application to an already-deployed machine learning API. This closely resembles a production environment, where you have one or more machine learning models deployed, and the application development team wants to use them in their application. The only difference is that you're both the data science and application development team.

Once again, we'll have to make a couple of changes to the application structure:

  1. Let's start with the simpler part. Inside the root directory, create a Python file called predictor.py. This file will hold a single function that implements the logic discussed at the beginning of this chapter when we made predictions in the notebook environment.

    Put simply, this function has to make a POST request to the API and return a response in JSON format.

    Here's the entire code snippet for the file:

    import os
    import json...