MLOps Engineering on AWS

MLOps Engineering on AWS is an intermediate-level course that extends DevOps practices to the building, training, testing, deployment, and operation of machine learning models. The course focuses on the importance of data, models, and code, along with automation and collaboration across data engineering, data science, development, and operations teams.

Participants work with Amazon SageMaker and other AWS tools to automate ML workflows, deploy models using different strategies, and monitor production models for data drift, bias, resource consumption, and latency. The course also covers model packaging, inference, edge deployment, A/B testing, troubleshooting, and human-in-the-loop processes. Hands-on labs, demonstrations, workbooks, and group activities are included.

• Describe machine learning operations and the goals of MLOps
• Understand the key differences between DevOps and MLOps
• Describe the machine learning workflow and the importance of communication
• Explain options for automating end-to-end ML workflows
• Identify key Amazon SageMaker features for MLOps automation
• Build automated ML processes for building, training, testing, and deploying models
• Apply different deployment strategies, inference approaches, and scaling considerations
• Deploy ML models using Amazon SageMaker and conduct A/B testing
• Monitor ML models for data drift, bias, resource consumption, and latency
• Integrate human-in-the-loop reviews into production ML operations

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)