MLOps Engineering on AWS

Course Duration

3 Days

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

AWS Technical Essentials, or equivalent experience DevOps Engineering on AWS, or equivalent experience Practical Data Science with Amazon SageMaker , or equivalent experience

Course Description

This course teaches students how to design and implement MLOps pipelines on AWS. Students learn to automate the machine learning lifecycle — from data preparation and model training through deployment, monitoring, and retraining — using services such as Amazon SageMaker, AWS Step Functions, and AWS CodePipeline.

Learning Objectives

• Describe machine learning operations
• Understand the key differences between DevOps and MLOps
• Describe the machine learning workflow
• Discuss the importance of communications in MLOps
• Explain end-to-end options for automation of ML workflows
• List key Amazon SageMaker features for MLOps automation
• Build an automated ML process that builds, trains, tests, and deploys models
• Build an automated ML process that retrains the model based on change(s) to the model code
• Identify elements and important steps in the deployment process
• Describe items that might be included in a model package, and their use in training or inference
• Recognize Amazon SageMaker options for selecting models for deployment, including support for ML frameworks and built-in algorithms or bring-your-own-models
• Differentiate scaling in machine learning from scaling in other applications
• Determine when to use different approaches to inference
• Discuss deployment strategies, benefits, challenges, and typical use cases
• Describe the challenges when deploying machine learning to edge devices
• Recognize important Amazon SageMaker features that are relevant to deployment and inference
• Describe why monitoring is important
• Detect data drifts in the underlying input data
• Demonstrate how to monitor ML models for bias
• Explain how to monitor model resource consumption and latency
• Discuss how to integrate human-in-the-loop reviews of model results in production

Course Outline

• 1 – Introduction to MLOps
• 2 – MLOps Development
• 3 – MLOps Deployment
• 4 – Model Monitoring and Operations