As a machine learning engineer or data scientist, most of your time is spent experimenting with machine learning models, for example adjusting parameters, comparing metrics, creating and saving visualizations, generating reports, etc. However, on many occasions we do not usually carry out this tracking in a healthy way. A healthy, simple and efficient way to carry out this tracking is by making use of tools that facilitate this type of activity, such is the case of ML_flow_.

In this blog, you will learn how to install, how track metrics, how to track parameters and how to save and reuse a scikit-learn ML model. So this blog is divided into the following sections:

• What is MLflow?
• Building a ML pipeline
• Tracking a ML pipeline
• Visualization

So, let’s get started!

#mlflow #scikit-learn #decision-tree #tracking #classification #machine-learning