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 15-minute podcast produced by Data Science Central, we explore best practices in Production Model Deployment. With MLOps, the goal is to make model deployment easy. Operations teams, not data scientists, can deploy models written in a variety of modern programming languages like Python and R onto modern runtime environments in the cloud or on-premise. Users of the MLOps system don’t have to know any of these technologies to drag and drop a model into the system, create a container, and deploy the model to a production environment.