Course Overview

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 hand-offs between data engineers, data scientists, software developers, and operations.

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Level: Intermediate

Duration: 3 Days

Delivery Type: Instructor-Led Training

Course Objectives

• Describe Machine Learning Operations
• Understand the key differences between DevOps and MLOps
• Describe the machine learning workflow
• Discuss the importance of communications in MLOps
• Explain end-to-end options for automation of ML workflows
• List key Amazon SageMaker features for MLOps automation
• Build an automated ML process that builds, trains, tests and deploys models
• Deployment operations
• Identify potential security threats in ML and explain basic mitigation approaches
• Describe why monitoring is important
• Detect data drifts in the underlying input data
• Demonstrate how to monitor ML models for bias
• Explain how to monitor model resource consumption and latency

Prerequisites

Required

• AWS Technical Essentials
• Practical Data Science with Amazon SageMaker
• DevOps Engineering on AWS

Recommended

• The Elements of Data Science (digital course), or equivalent experience
• Machine Learning Terminology and Process (digital course)

Who Should Go For This Training?

• DevOps Engineers
• ML Engineers
• Developers/Operations with responsibility for operationalizing ML models

Course Outline

Day 1

Module 1: Introduction

• Course introduction

Module 2: Introduction to MLOps

• Machine learning operations
• The goals of machine learning operations (MLOps)
• The path from DevOps to MLOps
• Machine learning
• Scope
• An MLOps view of the Machine learning workflow
• Communication
• The value of MLOps: MLOps cases

Day 2

Module 3: MLOps Development

• Intro to build, train, and evaluate machine learning models
• Automating
• Apache Airflow
• Kubernetes integration for MLOps
• Amazon SageMaker for MLOps
• Demonstration: Amazon SageMaker
• Intro to build, train, and evaluate machine learning models
• Demonstration: Lab overview
• Lab: Bring your own algorithm to an MLOps pipeline
• Group Activity: MLOps Action Plan Workbook
• Lab: Code and serve your ML model with AWS CodeBuild

Module 4: MLOps Deployment

• Introduction to deployment operations
• Model packaging
• Inference
• Lab: Deploy your model to production
• SageMaker production variants
• Deployment strategies
• Deploying to the edge
• Deployment security
• Lab: Conduct A/B testing
• Group Activity: MLOps Action Plan Workbook

Day 3

Module 5: Model Monitoring and Operations

• The importance of monitoring
• Monitoring by design
• Lab: Monitor your ML model
• Human-in-the-loop
• Amazon SageMaker Model Monitor
• Demo: Amazon SageMaker Model Monitor
• Solving the Problem(s)
• Group Activity: MLOps Action Plan Workbook

Module 6: Wrap-up

• Course review
• Group Activity: MLOps Action Plan Workbook
• Wrap-up