MLflow: A platform for managing the machine learning lifecycle

Check out the "Model lifecycle management" sessions at the Strata Data Conference in New York, September 11-13, 2018. Hurry--early price ends July 27. Although machine learning (ML) can produce fantastic results, using it in practice is complex. Beyond the usual challenges in software development, machine learning developers face new challenges, including experiment management (tracking which parameters, code, and data went into a result); reproducibility (running the same code and environment later); model deployment into production; and governance (auditing models and data used throughout an organization). These workflow challenges around the ML lifecycle are often the top obstacle to using ML in production and scaling it up within an organization.