MLOps Engineering on AWS

• 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

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)

Day 1

Module 0: Welcome

• Course introduction

Module 1: Introduction to MLOps

• Machine learning operations
• Goals of MLOps
• Communication
• From DevOps to MLOps
• ML workflow
• Scope
• MLOps view of ML workflow
• MLOps cases

Module 2: MLOps Development

• Intro to build, train, and evaluate machine learning models
• MLOps security
• Automating
• Apache Airflow
• Kubernetes integration for MLOps
• Amazon SageMaker for MLOps
• Lab: Bring your own algorithm to an MLOps pipeline
• Demonstration: Amazon SageMaker
• Intro to build, train, and evaluate machine learning models
• Lab: Code and serve your ML model with AWS CodeBuild
• Activity: MLOps Action Plan Workbook

Day 2

Module 3: MLOps Deployment

• Introduction to deployment operations
• Model packaging
• Inference
• Lab: Deploy your model to production
• SageMaker production variants
• Deployment strategies
• Deploying to the edge
• Lab: Conduct A/B testing
• Activity: MLOps Action Plan Workbook

Day 3

Module 4: Model Monitoring and Operations

• Lab: Troubleshoot your pipeline
• The importance of monitoring
• Monitoring by design
• Lab: Monitor your ML model
• Human-in-the-loop
• Amazon SageMaker Model Monitor
• Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store
• Solving the Problem(s)
• Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

• Course review
• Activity: MLOps Action Plan Workbook
• Wrap-up

Labs - Please note: The labs for your AWS course will be delivered through AWS Builder labs. In order to access these labs you will need to have an Amazon BuilderID. You can set up your new Amazon account here. Please ensure that you have set up this Amazon BuilderID in advance of attending your class.

Courseware – Please note: In order to access your digital course materials you are required to set up a Gilmore account in advance of attending your course. To do this please follow this link.

Please also be aware that in order to access your materials and Labs it is important that your device and network should not restrict access to AWS or Vitalsource content. For that reason, AWS recommend NOT using a Corporate laptop with any security restrictions in place or the use of a VPN.