Module 1: Amazon SageMaker Setup and Navigation

• Launch SageMaker Studio using the AWS Service Catalog.
• Navigate the SageMaker Studio interface.
• Demo: SageMaker Studio UI walkthrough.
• Lab 1: Launch SageMaker Studio from AWS Service Catalog.

Module 2: Data Processing

• Collect, clean, visualize, analyze, and transform data with SageMaker Studio.
• Establish repeatable data processing workflows.
• Validate data readiness for ML and detect bias.
• Estimate baseline model accuracy.
• Lab 2: Data preparation using SageMaker Data Wrangler.
• Lab 3: Scalable data preparation with Amazon EMR.
• Lab 4: Data processing using SageMaker Processing and the SageMaker Python SDK.
• Lab 5: Feature engineering with SageMaker Feature Store.

Module 3: Model Development

• Develop, tune, and evaluate ML models with SageMaker Studio.
• Optimize hyperparameters automatically.
• Use SageMaker Debugger for issue detection.
• Ensure models adhere to fairness and explainability practices.
• Demo: Autopilot.
• Lab 6: Track model training and tuning iterations with SageMaker Experiments.
• Lab 7: Analyze and set alerts using SageMaker Debugger.
• Lab 8: Identify bias with SageMaker Clarify.

Module 4: Deployment and Inference

• Manage model versions and approval status with SageMaker Model Registry.
• Design and implement inference deployment solutions.
• Automate ML workflows with SageMaker Pipelines.
• Lab 9: Perform inference with SageMaker Studio.
• Lab 10: Integrate SageMaker Pipelines and Model Registry in SageMaker Studio.

Module 5: Monitoring

• Configure SageMaker Model Monitor for data quality, model quality, and drift detection.
• Set up monitoring schedules with predefined intervals.
• Demo: Model monitoring.

Module 6: Managing SageMaker Studio Resources and Updates

• List resources and manage costs.
• Shut down instances, notebooks, terminals, and kernels.
• Update SageMaker Studio effectively.