March Online Meeting: How to Reduce Scikit-Learn Training Time
This month, we'll have a talk from Michael Galarnyk of Anyscale about three ways to reduce the time it takes to do training for the popular Scikit-Learn machine learning library.
- Video of Presentation
- From YouTube
Scikit-Learn is an easy to use Python library for machine learning. However, sometimes scikit-learn models can take a long time to train. There are quite a few approaches to solving this problem like:
- Changing your optimization function (solver)
- Using different hyperparameter optimization techniques (grid search, random search, early stopping, etc)
- Parallelize or distribute your training with joblib and Ray
Attendees will learn about each approach, the advantages and disadvantages of each, and how to speed up their scikit-learn workflow.
Michael Galarnyk works in Developer Relations at Anyscale, the company behind the Ray Project. In his spare time, he teaches Python based Machine Learning classes through Stanford Continuing Studies and LinkedIn Learning. You can find him on Twitter (https://twitter.com/GalarnykMichael), Medium (https://medium.com/@GalarnykMichael), and GitHub (https://github.com/mGalarnyk).