AMA27 -MLOps Engineering on AWS
AMA27 -MLOps Engineering on AWS
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.
Activities
This course includes presentations, hands-on labs, demonstrations, knowledge checks, and workbook activities.
Course objectives
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 ofnewly acquired data
Intended 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
Course 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