Which feature allows tracking of experiments and metrics in Azure ML?

Azure Machine Learning integrates with MLflow, an open-source platform for managing the ML lifecycle. MLflow Tracking is a component of MLflow that is used to log and track training run metrics, parameters, artifacts, and models. When you submit a training job in Azure Machine Learning, it can automatically log this information, which can then be viewed and compared in the Azure Machine Learning studio, providing a comprehensive history and audit trail for all experiments.

Pipelines are used to define and automate machine learning workflows. While a pipeline run is an experiment that is tracked, the pipeline itself is the workflow definition.

Compute instances are cloud-based workstations for development. Experiments are run on them, but they are not the tracking feature itself.

Data labeling is a feature for annotating data, which is a step in data preparation, not experiment tracking.

1. Microsoft. (2023). Track ML experiments and models with MLflow. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs. (See the "Introduction" section, Paragraph 1, which states "Azure Machine Learning uses MLflow Tracking for logging and tracking experiments.").

2. Microsoft. (2023). Log & view metrics and log files. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics. (This page details how MLflow is the backend for logging).