Build, train, and deploy machine learning (ML) models.

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.

Who should take this course

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


Day 1

Module 0: Welcome

Course introduction
Module 1: Introduction to MLOps

Machine learning operations
Goals of MLOps
From DevOps to MLOps
ML workflow
MLOps view of ML workflow
MLOps cases
Module 2: MLOps Development

Intro to build, train, and evaluate machine learning models
MLOps security
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
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
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





Good understanding of DevOps and AWS architecture. AWS Technical Essentials DevOps Engineering on AWS Practical Data Science with Amazon SageMaker

Course Details

What You Will Learn?

In this course, you will learn to:

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