MLOps Engineering on AWS – MLOAWS001

Course Content

This course builds upon the principles of DevOps, extending them to the development, training, and deployment of machine learning (ML) models. It follows the four-level MLOps maturity framework, focusing on the first three levels: Initial, Repeatable, and Reliable. Emphasizing the critical roles of data, models, and code in successful ML deployments, the course showcases how tools, automation, processes, and collaboration address challenges during handoffs among data engineers, data scientists, developers, and operations teams. Additionally, it explores methods to monitor production models and respond to performance drifts relative to key performance indicators (KPIs).

Delivery Method

In-Person
Online
Private Team Training: Upskill your team with tailored training at your facility.

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Goals

- Articulate the benefits of MLOps and how it differs from DevOps.
- Assess security and governance needs for ML use cases, and identify solutions and mitigation strategies.
- Set up experimentation environments for MLOps using Amazon SageMaker.
- Apply best practices for versioning and maintaining data, model, and code integrity.
- Describe three methods for creating a full CI/CD pipeline in an ML context.
- Implement automated packaging, testing, and deployment processes for ML assets.
- Monitor ML-based solutions and automate re-training workflows to address performance degradation.

Pre Requisites

- To succeed in this course, participants should have completed the following or have equivalent experience:
  - Learning Tree Course 1226: AWS Technical Essentials
  - Learning Tree Course 1222: DevOps Engineering on AWS
  - Practical Data Science with Amazon SageMaker

Course Outline

Day 1: Foundations and Initial MLOps

- Module 1: Introduction to MLOps
  - Overview of processes, people, and technology in MLOps
  - Security and governance principles
  - MLOps maturity model
  Module 2: Initial MLOps – Experimentation Environments in SageMaker Studio
  - Introduction to experimentation in MLOps
  - Setting up ML experimentation environments
  - Demonstration: Creating and updating lifecycle configurations in SageMaker Studio
  - Hands-On Lab: Provisioning SageMaker Studio environments using AWS Service Catalog
  - Workbook: Initial MLOps
  Module 3: Repeatable MLOps – Repositories
  - Data management strategies for MLOps
  - Version control for ML models
  - Using repositories for code and models
  Module 4: Repeatable MLOps – Orchestration
  - Building ML pipelines
  - Demonstration: Orchestrating model building pipelines with SageMaker Pipelines

Day 2: Repeatable and Reliable MLOps

- Module 4: Orchestration (continued)
  - End-to-end orchestration with AWS Step Functions
  - Hands-On Lab: Automating workflows with Step Functions
  - Standardizing ML pipelines with SageMaker Projects
  - Demonstration: Using SageMaker Projects for end-to-end pipeline standardization
  - Exploring third-party tools for repeatability
  - Governance and security in MLOps
  - Demonstration: Security best practices in SageMaker
  - Workbook: Repeatable MLOps
  Module 5: Reliable MLOps – Scaling and Testing
  - Scaling strategies and multi-account deployment
  - Testing and traffic-shifting for reliability
  - Demonstration: Leveraging SageMaker Inference Recommender
  - Hands-On Lab: Testing and deploying model variants

Day 3: Reliable MLOps - Advanced Scaling, Testing, and Monitoring

- Module 5: Scaling and Testing (continued)
  - Hands-On Lab: Traffic shifting and multi-account strategies
  - Workbook: Reliable MLOps
  Module 6: Reliable MLOps – Monitoring
  - Importance of monitoring ML solutions
  - Hands-On Lab: Monitoring for data drift
  - Operational considerations for model monitoring
  - Addressing issues identified through monitoring
  - Hands-On Lab: Building and troubleshooting ML pipelines
  - Workbook: Reliable MLOps

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