MLOEAWAmazon Web ServicesAW-MLOE2.0<p>Erforderlich:</p> <ul> <li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/course/amazon-awse"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>AWS Technical Essentials <span class="fl-prod-pcode">(AWSE)</span></a></span></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/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></li><li><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/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></li></ul><p>Zus&auml;tzlich Empfohlen:</p> <ul> <li>The Elements of Data Science (digitaler Kurs) oder gleichwertige Erfahrung</li><li>Machine Learning Terminology and Process (digitaler Kurs)</li></ul><ul> <li>DevOps Engineers</li><li>ML Engineers</li><li>Entwickler/Betriebe mit Verantwortung f&uuml;r die Operationalisierung von ML-Modellen</li></ul><ul> <li>Module 0: Welcome</li><li>Module 1: Introduction to MLOps</li><li>Module 2: MLOps Development</li><li>Module 3: MLOps Deployment</li><li>Module 4: Model Monitoring and Operations</li><li>Module 5: Wrap-up</li></ul><h5>Module 0: Welcome</h5><ul> <li>Course introduction</li></ul><h5>Module 1: Introduction to MLOps</h5><ul> <li>Machine learning operations</li><li>Goals of MLOps</li><li>Communication</li><li>From DevOps to MLOps</li><li>ML workflow</li><li>Scope</li><li>MLOps view of ML workflow</li><li>MLOps cases</li></ul><h5>Module 2: MLOps Development</h5><ul> <li>Intro to build, train, and evaluate machine learning models</li><li>MLOps security</li><li>Automating</li><li>Apache Airflow</li><li>Kubernetes integration for MLOps</li><li>Amazon SageMaker for MLOps</li><li>Lab: Bring your own algorithm to an MLOps pipeline</li><li>Demonstration: Amazon SageMaker</li><li>Intro to build, train, and evaluate machine learning models</li><li>Lab: Code and serve your ML model with AWS CodeBuild</li><li>Activity: MLOps Action Plan Workbook</li></ul><h5>Module 3: MLOps Deployment</h5><ul> <li>Introduction to deployment operations</li><li>Model packaging</li><li>Inference</li><li>Lab: Deploy your model to production</li><li>SageMaker production variants</li><li>Deployment strategies</li><li>Deploying to the edge</li><li>Lab: Conduct A/B testing</li><li>Activity: MLOps Action Plan Workbook</li></ul><h5>Module 4: Model Monitoring and Operations</h5><ul> <li>Lab: Troubleshoot your pipeline</li><li>The importance of monitoring</li><li>Monitoring by design</li><li>Lab: Monitor your ML model</li><li>Human-in-the-loop</li><li>Amazon SageMaker Model Monitor</li><li>Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store</li><li>Solving the Problem(s)</li><li>Activity: MLOps Action Plan Workbook</li></ul><h5>Module 5: Wrap-up</h5><ul> <li>Course review</li><li>Activity: MLOps Action Plan Workbook</li><li>Wrap-up</li></ul>Erforderlich: - AWS Technical Essentials (AWSE) - DevOps Engineering on AWS (AWSDEVOPS) - Practical Data Science with Amazon SageMaker (PDSASM) Zusätzlich Empfohlen: - The Elements of Data Science (digitaler Kurs) oder gleichwertige Erfahrung - Machine Learning Terminology and Process (digitaler Kurs)- DevOps Engineers - ML Engineers - Entwickler/Betriebe mit Verantwortung für die Operationalisierung von ML-Modellen- Module 0: Welcome - Module 1: Introduction to MLOps - Module 2: MLOps Development - Module 3: MLOps Deployment - Module 4: Model Monitoring and Operations - Module 5: Wrap-upModule 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 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 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-up3 Tage1995.001650.001995.001995.002250.006920.001995.001995.001995.002095.002025.005200.002655.002470.002795.002450.002395.00