Experiment Tracking and Model Registry

Experiment Tracking is an essential part of MLOps, providing a system to monitor and record the different experiments conducted during the machine learning model development process. This involves capturing, organizing and visualizing the metadata associated with each experiment, such as hyperparameters used, models produced, metrics like accuracy or loss, and other information about the computational environment. This tracking allows for reproducibility of experiments, comparison across different experiment runs, and helps in identifying the best models.

Visit the following resources to learn more:

• @article@Experiment Tracking
• @article@ML Flow Model Registry