COURSE LIST / COURSE BY TECHNOLOGY VENDORS / AWS

MLOps Engineering on AWS

Course Summary:

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate 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 will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators. The instructor will encourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and instructors.

Course Duration: 1 Day

Course Content:

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

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