The machine learning pipeline doesn’t end at just building the model. This course will teach you how to deploy your machine learning models as application programming interface (API) endpoints, and the maintenance required to support the model.
Machine learning models only become useful once they begin to support the business through a deployed application. In this course, Model Deployment and Maintenance for Data Scientists, you’ll gain the ability to run, monitor, and optimize machine learning models in production. First, you’ll explore options for deploying machine learning models as an API endpoint. Next, you’ll discover metrics and KPIs for the model you will need to monitor. Finally, you’ll learn how to iterate and improve on your model as time goes on. When you’re finished with this course, you’ll have the skills and knowledge of deploying and maintaining machine learning models needed to productionalize your machine learning pipeline.
Machine learning models only become useful once they begin to support the business through a deployed application. In this course, Model Deployment and Maintenance for Data Scientists, you’ll gain the ability to run, monitor, and optimize machine learning models in production. First, you’ll explore options for deploying machine learning models as an API endpoint. Next, you’ll discover metrics and KPIs for the model you will need to monitor. Finally, you’ll learn how to iterate and improve on your model as time goes on. When you’re finished with this course, you’ll have the skills and knowledge of deploying and maintaining machine learning models needed to productionalize your machine learning pipeline.
