Live-Online: MLOps Engineering on AWS

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.

Day 1
1. Introduction to MLOps

• Processes
• People
• Technology
• Security and governance
• MLOps maturity model
   

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
   

3. Repeatable MLOps: Repositories

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

4. Repeatable MLOps: Orchestration

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

Day 2
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
   

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
5. Reliable MLOps: Scaling and Testing (continued)

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

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

• Explaining the benefits of MLOps
• Comparing and contrasting DevOps and MLOps
• Evaluating the security and governance requirements for an ML use case and describing possible solutions and mitigation strategies
• Setting up experimentation environments for MLOps with Amazon SageMaker
• Explaining best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)
• Describing three options for creating a full CI/CD pipeline in an ML context
• Recalling best practices for implementing automated packaging, testing and deployment (Data/model/code)
• Demonstrating how to monitor ML based solutions
• Demonstrating 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

This course is intended for the following job roles:

• DevOps
• Machine Learning & AI