Microsoft has been serious about helping data scientists track and manage their machine learning experiments for some time now. For example, the company’s Azure Machine Learning (Azure ML) cloud service has supported the logging of experiments, including iterative runs with varying algorithms, hyperparameter values, or both.
While Azure ML has had its own framework for such experiment monitoring and tracking, at last year’s Spark+AI Summit, its partner Databricks launched the open source MLflow project for handling similar tasks. MLflow is designed to work from most any environment, including the command line, notebooks and more, and its popularity has grown impressively over the last year, ostensibly as a result of that open orientation. READ MORE ON: ZD NET