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.
Author Name: Miguel Saavedra
Author Description:
Miguel Saavedra is an author, solutions architect, and Instructor specializing in AWS, big data, automation, and security. He has worked in several companies in the finance/fintech, education, and medical industries as well as some government projects. He has published and conducted research on both cloud and on-premise solutions for big data focusing on network analytics, and machine learning. He also designs highly available and automated CICD toolchains for high throughput microservices on AW… more