Report Generation
Explore how to generate and manage reports within ML pipelines. Understand the process of experiment tracking by exporting training metrics, saving metric files, and visualizing outcomes such as confusion matrices to compare different model runs effectively.
We'll cover the following...
We'll cover the following...
Each run of an ML pipeline must be associated with an export of training metrics. This is done so that data scientists can run multiple experiments and compare results.
Experiment tracking
Consider the following scenario: We’re doing a classification problem, and our baseline model has an accuracy of 92%. To try to improve the accuracy, we change a hyperparameter and train the model again. This time, we get an accuracy of 91%. Changing another ...