Snap ML is a library for accelerated training and inference of Machine Learning models such as linear models, decision trees, random forests, and boosting machines. It’s a library developed and maintained by IBM Research. The library binaries are freely available on PyPi. It has support for Linux/x86, Linux/Power, MacOS, Windows, Linux/Z. GPU support is also available for Linux. If you are curious, you can find detailed documentation here and usage examples here.

Snap ML provides highly-efficient CPU/GPU implementations of linear models and tree-based models. It not only accelerates Machine Learning algorithms through system awareness but also offers novel Machine Learning algorithms with best-in-class accuracy. In this guided project, we will focus on training acceleration in particular. You will consolidate your Machine Learning modeling skills by using a popular regression model: Decision Tree. You will use a real-world dataset to train such a model. 

You will find out that a Scikit-learn application can be seamlessly optimized by using Snap ML. The seamless integration of the Snap ML library is possible due to its Scikit-learn Python API compatibility.

To complete this guided project, you will need a basic understanding of the working mechanics of the Decision Tree models. You will also need some prior experience working with Scikit-learn APIs to be able to follow our data preprocessing steps easily.

This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.

Frequently Asked Questions

Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.

What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.