Curriculum
- 3 Sections
- 8 Lessons
- 3 Days
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- Day 14
- 1.1Module 1: Introduction to MLOps Processes People Technology Security and governance MLOps maturity model
- 1.2Module 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
- 1.3Module 3: Repeatable MLOps: Repositories Managing data for MLOps Version control of ML models Code repositories in ML
- 1.4Module 4: Repeatable MLOps: Orchestration ML pipelines Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines
- Day 22
- 2.1Module 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
- 2.2Module 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 32
- 3.1Module 5: Reliable MLOps: Scaling and Testing (continued) Hands-On Lab: Shifting Traffic Workbook: Multi-account strategies
- 3.2Module 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
Module 4: Repeatable MLOps: Orchestration ML pipelines Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines
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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
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