How to Track and Visualize Machine Learning Experiments using MLflow

Author(s): Gurami Keretchashvili

Originally published on Towards AI.

MLflow — an open-source platform to manage machine learning lifecycle.

In machine learning there is no free lunch. We do not know which data preprocessing or which machine learning algorithm is the best for the specific problem. There is no one unique algorithm that performs best. That is why experimenting is the typical methods to achieve the appropriate result. To do effective machine learning experiments we need to track, remember and visualize each of the experimental run.

MLflow ui visualization example (gif by author)

What — is experiment tracking?

Why — experiment tracking is important?

How — to do it?

Practical Demo of experimental tracking using MLFlow

Experiment tracking is the process of keeping track of… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI