Understanding machine learning model governance

In this episode of the Data Exchange I speak with Harish Doddi, cofounder of Datatron, a startup focused on helping companies operationalize machine learning. Over the past two years, Harish has worked closely with enterprises to understand their needs in the areas of model operations and model governance. Last year Harish and I, along with David Talby, wrote two articles on these topics. In the first article, we described these emerging areas (“What are model governance and model operations?”),  and in the second we listed lessons that ML engineers can draw from two highly regulated industries (“Managing machine learning in the enterprise: Lessons from banking and health care”).As machine learning becomes widely deployed, organizations will need to develop processes and tools to ensure that models behave as intended. This means having the right set of controls and validation steps in place.Our conversation focused on model governance and related topics:We discussed the three related areas of MLOps, Model Governance, Model Observability.I asked Harish to describe how model governance is perceived and practiced in different industries.We discussed real-world examples of model governance, and organizational and staffing considerations that come into play.CI/CD for machine learning.Key enterprise features for model governance solutions.Detailed show notes, including a full transcript, can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.