PDSASMAWAmazon Web ServicesAW-PDSASM3<p>In this course, you will learn to: </p> <ul> <li>Discuss the benefits of different types of machine learning for solving business problems</li><li>Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems</li><li>Explain how data scientists use AWS tools and ML to solve a common business problem</li><li>Summarize the steps a data scientist takes to prepare data</li><li>Summarize the steps a data scientist takes to train ML models</li><li>Summarize the steps a data scientist takes to evaluate and tune ML models</li><li>Summarize the steps to deploy a model to an endpoint and generate predictions</li><li>Describe the challenges for operationalizing ML models</li><li>Match AWS tools with their ML function</li></ul><p>We recommend that attendees of this course have: </p> <ul> <li>AWS Technical Essentials</li><li>Entry-level knowledge of Python programming</li><li>Entry-level knowledge of statistics</li></ul><p>This course is intended for: </p> <ul> <li>Development Operations (DevOps) engineers</li><li>Application developers</li></ul><h5>Module 1: Introduction to Machine Learning</h5><ul> <li>Benefits of machine learning (ML)</li><li>Types of ML approaches</li><li>Framing the business problem</li><li>Prediction quality</li><li>Processes, roles, and responsibilities for ML projects</li></ul><h5>Module 2: Preparing a Dataset</h5><ul> <li>Data analysis and preparation</li><li>Data preparation tools</li><li>Demonstration: Review Amazon SageMaker Studio and Notebooks</li><li>Hands-On Lab: Data Preparation with SageMaker Data Wrangler</li></ul><h5>Module 3: Training a Model</h5><ul> <li>Steps to train a model</li><li>Choose an algorithm</li><li>Train the model in Amazon SageMaker</li><li>Hands-On Lab: Training a Model with Amazon SageMaker</li><li>Amazon CodeWhisperer</li><li>Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks</li></ul><h5>Module 4: Evaluating and Tuning a Model</h5><ul> <li>Model evaluation</li><li>Model tuning and hyperparameter optimization</li><li>Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker</li></ul><h5>Module 5: Deploying a Model</h5><ul> <li>Model deployment</li><li>Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction</li></ul><h5>Module 6: Operational Challenges</h5><ul> <li>Responsible ML</li><li>ML team and MLOps</li><li>Automation</li><li>Monitoring</li><li>Updating models (model testing and deployment)</li></ul><h5>Module 7: Other Model-Building Tools</h5><ul> <li>Different tools for different skills and business needs</li><li>No-code ML with Amazon SageMaker Canvas</li><li>Demonstration: Overview of Amazon SageMaker Canvas</li><li>Amazon SageMaker Studio Lab</li><li>Demonstration: Overview of SageMaker Studio Lab</li><li>(Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint</li></ul>In this course, you will learn to: - Discuss the benefits of different types of machine learning for solving business problems - Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems - Explain how data scientists use AWS tools and ML to solve a common business problem - Summarize the steps a data scientist takes to prepare data - Summarize the steps a data scientist takes to train ML models - Summarize the steps a data scientist takes to evaluate and tune ML models - Summarize the steps to deploy a model to an endpoint and generate predictions - Describe the challenges for operationalizing ML models - Match AWS tools with their ML functionWe recommend that attendees of this course have: - AWS Technical Essentials - Entry-level knowledge of Python programming - Entry-level knowledge of statisticsThis course is intended for: - Development Operations (DevOps) engineers - Application developersModule 1: Introduction to Machine Learning - Benefits of machine learning (ML) - Types of ML approaches - Framing the business problem - Prediction quality - Processes, roles, and responsibilities for ML projects Module 2: Preparing a Dataset - Data analysis and preparation - Data preparation tools - Demonstration: Review Amazon SageMaker Studio and Notebooks - Hands-On Lab: Data Preparation with SageMaker Data Wrangler Module 3: Training a Model - Steps to train a model - Choose an algorithm - Train the model in Amazon SageMaker - Hands-On Lab: Training a Model with Amazon SageMaker - Amazon CodeWhisperer - Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks Module 4: Evaluating and Tuning a Model - Model evaluation - Model tuning and hyperparameter optimization - Hands-On Lab: Model Tuning and Hyperparameter Optimization with Amazon SageMaker Module 5: Deploying a Model - Model deployment - Hands-On Lab: Deploy a Model to a Real-Time Endpoint and Generate a Prediction Module 6: Operational Challenges - Responsible ML - ML team and MLOps - Automation - Monitoring - Updating models (model testing and deployment) Module 7: Other Model-Building Tools - Different tools for different skills and business needs - No-code ML with Amazon SageMaker Canvas - Demonstration: Overview of Amazon SageMaker Canvas - Amazon SageMaker Studio Lab - Demonstration: Overview of SageMaker Studio Lab - (Optional) Hands-On Lab: Integrating a Web Application with an Amazon SageMaker Model Endpoint1 day675.00590.00750.00750.00795.00495.00495.00465.00465.00465.00495.00495.00750.002000.00600.00750.002610.00750.00750.00750.00900.00870.00930.00865.00