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Machine Learning Engineer
ML data platform engineers, DevOps engineers, and developers/operations staff with responsibility for operationalizing ML models will learn to address the challenges associated with handoffs between data engineers, data scientists, software developers, and operations through the use of tools, automation, processes, and teamwork. By the end of the course, go from learning to doing by building an MLOps action plan for your organization.
Day 1
Module 0: Welcome
- Course introduction
Module 1: Introduction to MLOps
- Machine learning operations
- Goals of MLOps
- Communication
- From DevOps to MLOps
- ML workflow
- Scope
- MLOps view of ML workflow
- MLOps cases
Module 2: MLOps Development
- Intro to build, train, and evaluate machine learning models
- MLOps security
- Automating
- Apache Airflow
- Kubernetes integration for MLOps
- Amazon SageMaker for MLOps
- Lab: Bring your own algorithm to an MLOps pipeline
- Demonstration: Amazon SageMaker
- Intro to build, train, and evaluate machine learning models
- Lab: Code and serve your ML model with AWS CodeBuild
- Activity: MLOps Action Plan Workbook
Day 2
Module 3: MLOps Deployment
- Introduction to deployment operations
- Model packaging
- Inference
- Lab: Deploy your model to production
- SageMaker production variants
- Deployment strategies
- Deploying to the edge
- Lab: Conduct A/B testing
- Activity: MLOps Action Plan Workbook
Day 3
Module 4: Model Monitoring and Operations
- Lab: Troubleshoot your pipeline
- The importance of monitoring
- Monitoring by design
- Lab: Monitor your ML model
- Human-in-the-loop
- Amazon SageMaker Model Monitor
- Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature
- Store
- Solving the Problem(s)
- Activity: MLOps Action Plan Workbook
Module 5: Wrap-up
- Course review
- Activity: MLOps Action Plan Workbook
- Wrap-up
- Deploying your own models in the AWS Cloud
- Automating workflows for building, training, testing, and deploying ML models
- The different deployment strategies for implementing ML models in production
- Monitoring for data drift and concept drift that could affect prediction and alignment with business expectations
This course can be used as preparation for the following official AWS Certification:
AWS Certified Machine Learning - Specialty.
This course includes instructor lecture, presentations, hands-on labs, demonstrations, and group exercises/discussions.
This course is intended for the following job roles:
- DevOps
- Machine Learning & AI
We recommend that attendees of this course have the following prerequisites:
- The Elements of Data Science (free digital course)
- Machine Learning Terminology and Process (free digital course)
and have attended the following course (or equivalent knowlege):
The Machine Learning Pipeline on AWS.
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