ML Ops — Model Tracking with Mlflow

Want to track machine learning models, the hyperparameter used, and their respective metrics then keep reading to explore mlflow.

In this article, you will learn the basics of model tracking, use Mlflow, an open-source tool for model tracking to monitor your machine learning/deep learning experiments.

Photo by Joss Woodhead on Unsplash

What is model tracking?

Model tracking is the ability to track changes made to your models to know the differences. Some of the main changes to track in a model are

• Model Architecture,
• Model Parameters value
• Model Metrics
• Data
• Dependent libraries

How does Model tracking help?

Model Tracking helps with

• Keeping track of all the experiments that you and your team might have performed for solving a problem using ML/DL
• Comparing the different model metrics to decide which model can be promoted for production deployment
• Ensuring the reproducibility of models at different locations or environments. Model tracking ensures that the model in a development environment is the…