MLOps Engineering on AWS
Official partner
AWS
Course Description
Could your Machine Learning (ML) workflow use some DevOps agility? MLOps Engineering on AWS will help you bring DevOps-style practices into the building, training, and deployment of ML models. ML data platform engineers, DevOps engineers, and developers/operations staff with responsibility for operationalizing ML models will learn to address the challenges associated with handoffs between data engineers, data scientists, software developers, and operations through the use of tools, automation, processes, and teamwork. By the end of the course, go from learning to doing by building an MLOps action plan for your organization.
Course Summary
Module 1: Introduction to MLOps
Module 2: Early MLOps: Experimentation Environments in SageMaker Studio
Module 3: Reproducible MLOps: Repositories
Module 4: Reproducible MLOps: Orchestration
Module 5: Reliable MLOps: Scaling and Testing
Module 6: Reliable MLOps: Monitoring
Prerequisites for this course
• AWS Technical Essentials course
• DevOps Engineering on AWS course, or equivalent experience
• Practical Data Science with Amazon SageMaker course, or equivalent experience
Audience for this course
• ML data platform engineers
• DevOps engineers
• Developers/operations staff with responsibility for operationalizing ML models