Course:

Course Description

This course builds upon and extends the DevOps methodology prevalent in software development to build, train, and deploy machine learning (ML) models. The course is based on the four-level MLOPs maturity framework. The course focuses on the first three levels, including the initial, repeatable, and reliable levels. The course stresses the importance of data, model, and code to successful ML deployments. It demonstrates 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 also discusses the use of tools and processes to monitor and take action when the model prediction in production drifts from agreed-upon key performance indicators.

Outline

Day 1

Module 1: Introduction to MLOps

Processes
People
Technology
Security and governance
MLOps maturity model

Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio

Bringing MLOps to experimentation
Setting up the ML experimentation environment
Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
Workbook: Initial MLOps

Module 3: Repeatable MLOps: Repositories

Managing data for MLOps
Version control of ML models
Code repositories in ML

Module 4: Repeatable MLOps: Orchestration

ML pipelines
Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines

Day 2

Module 4: Repeatable MLOps: Orchestration (continued)

End-to-end orchestration with AWS Step Functions
Hands-On Lab: Automating a Workflow with Step Functions
End-to-end orchestration with SageMaker Projects
Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects
Using third-party tools for repeatability
Demonstration: Exploring Human-in-the-Loop During Inference
Governance and security
Demonstration: Exploring Security Best Practices for SageMaker
Workbook: Repeatable MLOps

Module 5: Reliable MLOps: Scaling and Testing

Scaling and multi-account strategies
Testing and traffic-shifting
Demonstration: Using SageMaker Inference Recommender
Hands-On Lab: Testing Model Variants

Day 3

Module 5: Reliable MLOps: Scaling and Testing (continued)

Hands-On Lab: Shifting Traffic
Workbook: Multi-account strategies

Module 6: Reliable MLOps: Monitoring

The importance of monitoring in ML
Hands-On Lab: Monitoring a Model for Data Drift
Operations considerations for model monitoring
Remediating problems identified by monitoring ML solutions
Workbook: Reliable MLOps
Hands-On Lab: Building and Troubleshooting an ML Pipeline

Audience

This course is intended for:

MLOps engineers who want to productionize and monitor ML models in the AWS cloud
DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production

Prerequisites

We recommend that attendees of this course have:

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

What You Will Learn

In this course, you will learn to:

Explain the benefits of MLOps
Compare and contrast DevOps and MLOps
Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
Set up experimentation environments for MLOps with Amazon SageMaker
Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)
Describe three options for creating a full CI/CD pipeline in an ML context
Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code)
Demonstrate how to monitor ML based solutions
Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top of newly acquired data

AWS-MLO-ENG: MLOps Engineering on AWS