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MLOps Engineering on AWS

SS Course: 61931

Course Overview

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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.                                                                  

Scheduled Classes

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09/06/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
10/04/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
11/01/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
11/15/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
11/21/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led
12/13/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led

What You'll Learn

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Describe machine learning operations
  • Describe items that might be included in a model package, and their use in training or inference
  • Recognize Amazon SageMaker options for selecting models for deployment, including support for ML frameworks and built-in algorithms or bring-your-own-models
  • Differentiate scaling in machine learning from scaling in other applications
  • Determine when to use different approaches to inference
  • Discuss deployment strategies, benefits, challenges, and typical use cases
  • Describe the challenges when deploying machine learning to edge devices
  • Recognize important Amazon SageMaker features that are relevant to deployment and inference
  • Describe why monitoring is important
  • Detect data drifts in the underlying input data
  • Demonstrate how to monitor ML models for bias
  • Understand the key differences between DevOps and MLOps
  • Explain how to monitor model resource consumption and latency
  • Discuss how to integrate human-in-the-loop reviews of model results in production
  • 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
  • Build an automated ML process that retrains the model based on change(s) to the model code
  • Identify elements and important steps in the deployment process
  • In this course, you will learn to:

Outline

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Viewing outline for:
Course introduction
  • Machine learning operations
  • Goals of MLOps
  • Communication
  • From DevOps to MLOps
  • ML workflow
  • Scope
  • MLOps view of ML workflow
  • MLOps cases
  • Intro to build, train, and evaluate machine learning models
  • Lab: Code and serve your ML model with AWS CodeBuild
  • Activity: MLOps Action Plan Workbook
  • 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
  • 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
  • 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
  • Course review
  • Activity: MLOps Action Plan Workbook
  • Wrap-up

Prerequisites

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AWS Technical Essentials course (classroom or digital)
  • DevOps Engineering on AWS course, or equivalent experience
  • Practical Data Science with Amazon SageMaker course, or equivalent experience
  • Recommended: The Elements of Data Science (digital course), or equivalent experience
  • Recommended: Machine Learning Terminology and Process (digital course)
  • Required:

    Who Should Attend

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    DevOps engineers
    • ML engineers
    • Developers/operations with responsibility for operationalizing ML models
    • This course is intended for any one of the following roles with responsibility for productionizing machine learning models in the AWS Cloud:

    Next Step Courses

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