This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.
The instructor will encourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and instructors.
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Private training brings the classroom or virtual classroom experience to your office or a preferred offsite location. Convenient, practical, and effective training tailored to your company's needs.
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Module 0: Welcome
Module 1: Introduction to MLOps
Module 2: MLOps Development
Module 3: MLOps Deployment
Module 4: Model Monitoring and Operations
Module 5: Wrap-up
This course is intended for any one of the following roles with responsibility for productionizing machine learning models in the AWS Cloud: