Course description

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.

Course level: Intermediate

Duration 3 days

Activities

This course includes presentations, labs, demonstrations, workbooks, and group exercises.

Intended audience

This course is intended for any one of the following roles with responsibility for productionizing machine learning models in the AWS Cloud:

• DevOps engineers
• ML engineers
• Developers/operations with responsibility for operationalizing ML models

Prerequisites

Required

• AWS Technical Essentials course (classroom or digital)
• DevOps Engineering on AWS course, or equivalent experience
• Practical Data Science with Amazon SageMaker course, or equivalent experience

Recommended

• The Elements of Data Science (digital course), or equivalent experience
• Machine Learning Terminology and Process (digital course)

Course Outline

• Module 1: Introduction to MLOps
• Module 2: MLOps Development
• Module 3: MLOps Deployment
• Module 4: Model Monitoring and Operations
• Module 5: Wrap-up