Introduction
Imagine you're a data scientist for a company developing an application for a cancer research lab. The application is to be used by researchers who upload an image of tissue to determine whether or not it's healthy tissue. You're asked to train the model to detect breast cancer on a large image database that contains examples of healthy and unhealthy tissue
You're familiar with Jupyter notebooks, which you plan to use to develop the model. However, you want to periodically retrain the model to create a better performing model that must then be deployed so that researchers can use the model in the application they're using.
You'll learn how to track model training in notebooks with MLflow in Azure Machine Learning.
Learning objectives
In this module, you'll learn how to:
- Configure MLflow to use in notebooks
- Use MLflow for model tracking in notebooks