ASSDSAWAmazon Web ServicesAW-ASSDS1.0<h4>In this course, you will learn to:</h4><ul> <li>Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio</li></ul><p>We recommend that all attendees of this course have: </p> <ul> <li>Experience using ML frameworks</li><li>Python programming experience</li><li>At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models</li><li>AWS Technical Essentials digital or classroom training</li></ul><p>Experienced data scientists who are proficient in ML and deep learning fundamentals.</p><p><strong>Day 1</strong></p> <h5>Module 1: Amazon SageMaker Studio Setup</h5><ul> <li>JupyterLab Extensions in SageMaker Studio</li><li>Demonstration: SageMaker user interface demo</li></ul><h5>Module 2: Data Processing</h5><ul> <li>Using SageMaker Data Wrangler for data processing</li><li>Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler</li><li>Using Amazon EMR</li><li>Hands-On Lab: Analyze and prepare data at scale using Amazon EMR</li><li>Using AWS Glue interactive sessions</li><li>Using SageMaker Processing with custom scripts</li><li>Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK</li><li>SageMaker Feature Store</li><li>Hands-On Lab: Feature engineering using SageMaker Feature Store</li></ul><h5>Module 3: Model Development</h5><ul> <li>SageMaker training jobs</li><li>Built-in algorithms</li><li>Bring your own script</li><li>Bring your own container</li><li>SageMaker Experiments</li><li>Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models</li></ul><p><strong>Day 2</strong></p> <h5>Module 3: Model Development (continued)</h5><ul> <li>SageMaker Debugger</li><li>Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger</li><li>Automatic model tuning</li><li>SageMaker Autopilot: Automated ML</li><li>Demonstration: SageMaker Autopilot</li><li>Bias detection</li><li>Hands-On Lab: Using SageMaker Clarify for Bias and Explainability</li><li>SageMaker Jumpstart</li></ul> <h5>Module 4: Deployment and Inference</h5><ul> <li>SageMaker Model Registry</li><li>SageMaker Pipelines</li><li>Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio</li><li>SageMaker model inference options</li><li>Scaling</li><li>Testing strategies, performance, and optimization</li><li>Hands-On Lab: Inferencing with SageMaker Studio</li></ul> <h5>Module 5: Monitoring</h5><ul> <li>Amazon SageMaker Model Monitor</li><li>Discussion: Case study</li><li>Demonstration: Model Monitoring</li></ul><p><strong>Day 3</strong></p> <h5>Module 6: Managing SageMaker Studio Resources and Updates</h5><ul> <li>Accrued cost and shutting down</li><li>Updates</li></ul> <h5>Capstone</h5><ul> <li>Environment setup</li><li>Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler</li><li>Challenge 2: Create feature groups in SageMaker Feature Store</li><li>Challenge 3: Perform and manage model training and tuning using SageMaker Experiments</li><li>(Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization</li><li>Challenge 5: Evaluate the model for bias using SageMaker Clarify</li><li>Challenge 6: Perform batch predictions using model endpoint</li><li>(Optional) Challenge 7: Automate full model development process using SageMaker Pipeline</li></ul>In this course, you will learn to: - Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker StudioWe recommend that all attendees of this course have: - Experience using ML frameworks - Python programming experience - At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models - AWS Technical Essentials digital or classroom trainingExperienced data scientists who are proficient in ML and deep learning fundamentals.Day 1 Module 1: Amazon SageMaker Studio Setup - JupyterLab Extensions in SageMaker Studio - Demonstration: SageMaker user interface demo Module 2: Data Processing - Using SageMaker Data Wrangler for data processing - Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler - Using Amazon EMR - Hands-On Lab: Analyze and prepare data at scale using Amazon EMR - Using AWS Glue interactive sessions - Using SageMaker Processing with custom scripts - Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK - SageMaker Feature Store - Hands-On Lab: Feature engineering using SageMaker Feature Store Module 3: Model Development - SageMaker training jobs - Built-in algorithms - Bring your own script - Bring your own container - SageMaker Experiments - Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models Day 2 Module 3: Model Development (continued) - SageMaker Debugger - Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger - Automatic model tuning - SageMaker Autopilot: Automated ML - Demonstration: SageMaker Autopilot - Bias detection - Hands-On Lab: Using SageMaker Clarify for Bias and Explainability - SageMaker Jumpstart Module 4: Deployment and Inference - SageMaker Model Registry - SageMaker Pipelines - Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio - SageMaker model inference options - Scaling - Testing strategies, performance, and optimization - Hands-On Lab: Inferencing with SageMaker Studio Module 5: Monitoring - Amazon SageMaker Model Monitor - Discussion: Case study - Demonstration: Model Monitoring Day 3 Module 6: Managing SageMaker Studio Resources and Updates - Accrued cost and shutting down - Updates Capstone - Environment setup - Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler - Challenge 2: Create feature groups in SageMaker Feature Store - Challenge 3: Perform and manage model training and tuning using SageMaker Experiments - (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization - Challenge 5: Evaluate the model for bias using SageMaker Clarify - Challenge 6: Perform batch predictions using model endpoint - (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline3 jours1990.001990.001990.002025.001650.001800.002655.002195.002480.002795.001990.00