Many models created by data science teams never generate value because they never reach production. In the end, data scientists have to help deploy and maintain their models, which is costly and takes away from doing new data science.
What if there was a better way? Machine learning operations (MLOps) practices and technology help bridge the gap between data science and IT so that IT operations can deploy and manage models in production. In this latest Data Science Central podcast, we explore the best practices in four areas of MLOps: Production Model Deployment, Production Model Monitoring, Production Lifecycle Management, and Production Model Governance.
Dan Darnell, Senior Director on the MLOps Product Team – DataRobot
Sean Welch, Host and Producer – Data Science Central