Getting started

Intro to MyAutoML

There are a lot of great open source tools for data scientists to use. Basically all data scientists know pandas, numpy and scikit-learn. Many will be familiar with tools such as MLflow and Hyperopt. However, while all these packages provide great functionalities, we still have to tie them all together when we want to build a functioning data science product. For this, many of us will be familiar with the feeling we’re doing the same things over and over again.

MyAutoML aims to fill this gap: allowing data scientists to focus on what makes their individual projects unique.

MyAutoML focuses on scikit-learn type data science projects of classification and regression:


What makes your project unique? Indeed, the data. The overarching process and the algorithms tend to be mostly the same for every project. Scikit-learn and other open source packages provides the algorithms. MyAutoML aims to cover the process tying everything together, so you as a data scientist can focus on what’s most important:

  • translating your business problem into an analytics problem,

  • preparing the target variable and features you need,

  • generating business value.

What to expect

Perhaps it is easier to start off with what not to expect. MyAutoML is not a port of AutoML as offered by the Amazons, Googles and Microsofts of this world to your local environment. Perhaps one day in the future we may go in that direction, but at least for now we offer you tools (functions, classes and template scripts) to automate most of the repetitive work you do for your projects.

To get the most out of MyAutoML you will need a basic infrastructure setup, built upon open source software, such as MLflow and Hyperopt. Please have a look at the Environment page for more information.

Quick questions

The simplest way to install MyAutoML is to from PyPI via pip:

pip install myautoml

In the User Guide we have included a Glossary.