MLOEAWAmazon Web ServicesAW-MLOE2.0<p>In this course, you will learn to: </p> <ul> <li>Explain the benefits of MLOps</li><li>Compare and contrast DevOps and MLOps</li><li>Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies</li><li>Set up experimentation environments for MLOps with Amazon SageMaker</li><li>Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)</li><li>Describe three options for creating a full CI/CD pipeline in an ML context</li><li>Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code)</li><li>Demonstrate how to monitor ML based solutions</li><li>Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top of newly acquired data</li></ul><p>We recommend that attendees of this course have </p> <ul> <li>AWS Technical Essentials course (classroom or digital)</li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/en/course/amazon-awsdevops"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>DevOps Engineering on AWS <span class="fl-prod-pcode">(AWSDEVOPS)</span></a></span>, or equivalent experience</li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/en/course/amazon-pdsasm"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Practical Data Science with Amazon SageMaker <span class="fl-prod-pcode">(PDSASM)</span></a></span>, or equivalent experience</li></ul><p>This course is intended for: </p> <ul> <li>MLOps engineers who want to productionize and monitor ML models in the AWS cloud</li><li>DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production</li></ul><p><strong>Day 1</strong></p> <h5>Module 1: Introduction to MLOps</h5><ul> <li>Processes</li><li>People</li><li>Technology</li><li>Security and governance</li><li>MLOps maturity model</li></ul><h5>Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio</h5><ul> <li>Bringing MLOps to experimentation</li><li>Setting up the ML experimentation environment</li><li>Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio</li><li>Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog</li><li>Workbook: Initial MLOps</li></ul> <h5>Module 3: Repeatable MLOps: Repositories</h5><ul> <li>Managing data for MLOps</li><li>Version control of ML models</li><li>Code repositories in ML</li></ul> <h5>Module 4: Repeatable MLOps: Orchestration</h5><ul> <li>ML pipelines</li><li>Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines</li></ul><p><strong>Day 2</strong></p> <h5>Module 4: Repeatable MLOps: Orchestration (continued)</h5><ul> <li>End-to-end orchestration with AWS Step Functions</li><li>Hands-On Lab: Automating a Workflow with Step Functions</li><li>End-to-end orchestration with SageMaker Projects</li><li>Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects</li><li>Using third-party tools for repeatability</li><li>Demonstration: Exploring Human-in-the-Loop During Inference</li><li>Governance and security</li><li>Demonstration: Exploring Security Best Practices for SageMaker</li><li>Workbook: Repeatable MLOps</li></ul> <h5>Module 5: Reliable MLOps: Scaling and Testing</h5><ul> <li>Scaling and multi-account strategies</li><li>Testing and traffic-shifting</li><li>Demonstration: Using SageMaker Inference Recommender</li><li>Hands-On Lab: Testing Model Variants</li></ul><p><strong>Day 3</strong></p> <h5>Module 5: Reliable MLOps: Scaling and Testing (continued)</h5><ul> <li>Hands-On Lab: Shifting Traffic</li><li>Workbook: Multi-account strategies</li></ul> <h5>Module 6: Reliable MLOps: Monitoring</h5><ul> <li>The importance of monitoring in ML</li><li>Hands-On Lab: Monitoring a Model for Data Drift</li><li>Operations considerations for model monitoring</li><li>Remediating problems identified by monitoring ML solutions</li><li>Workbook: Reliable MLOps</li><li>Hands-On Lab: Building and Troubleshooting an ML Pipeline</li></ul>In this course, you will learn to: - Explain the benefits of MLOps - Compare and contrast DevOps and MLOps - Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies - Set up experimentation environments for MLOps with Amazon SageMaker - Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code) - Describe three options for creating a full CI/CD pipeline in an ML context - Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code) - Demonstrate how to monitor ML based solutions - Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top of newly acquired dataWe recommend that attendees of this course have - AWS Technical Essentials course (classroom or digital) - DevOps Engineering on AWS (AWSDEVOPS), or equivalent experience - Practical Data Science with Amazon SageMaker (PDSASM), or equivalent experienceThis course is intended for: - MLOps engineers who want to productionize and monitor ML models in the AWS cloud - DevOps engineers who will be responsible for successfully deploying and maintaining ML models in productionDay 1 Module 1: Introduction to MLOps - Processes - People - Technology - Security and governance - MLOps maturity model Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio - Bringing MLOps to experimentation - Setting up the ML experimentation environment - Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio - Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog - Workbook: Initial MLOps Module 3: Repeatable MLOps: Repositories - Managing data for MLOps - Version control of ML models - Code repositories in ML Module 4: Repeatable MLOps: Orchestration - ML pipelines - Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines Day 2 Module 4: Repeatable MLOps: Orchestration (continued) - End-to-end orchestration with AWS Step Functions - Hands-On Lab: Automating a Workflow with Step Functions - End-to-end orchestration with SageMaker Projects - Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects - Using third-party tools for repeatability - Demonstration: Exploring Human-in-the-Loop During Inference - Governance and security - Demonstration: Exploring Security Best Practices for SageMaker - Workbook: Repeatable MLOps Module 5: Reliable MLOps: Scaling and Testing - Scaling and multi-account strategies - Testing and traffic-shifting - Demonstration: Using SageMaker Inference Recommender - Hands-On Lab: Testing Model Variants Day 3 Module 5: Reliable MLOps: Scaling and Testing (continued) - Hands-On Lab: Shifting Traffic - Workbook: Multi-account strategies Module 6: Reliable MLOps: Monitoring - The importance of monitoring in ML - Hands-On Lab: Monitoring a Model for Data Drift - Operations considerations for model monitoring - Remediating problems identified by monitoring ML solutions - Workbook: Reliable MLOps - Hands-On Lab: Building and Troubleshooting an ML Pipeline3 days1995.001650.001995.001995.002250.006920.001995.001995.001995.002095.002025.005200.002655.002470.002795.002450.002395.00