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The open-source MLflow REST API allows you to create, list, and get experiments and runs, and allows you to log parameters, metrics, and artifacts. The Databricks Runtime for Machine Learning provides a managed version of the MLflow server, which includes experiment tracking and the Model Registry.
For MLflow, there are two REST API reference guides:
Training
Module
Use MLflow in Azure Databricks - Training
Learn how to use MLflow in Azure Databricks to track machine learning experiments and deploy models.
Certification
Microsoft Certified: Azure Data Scientist Associate - Certifications
Manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning and MLflow.