• Launch SageMaker Studio from the AWS Service Catalog.
• Navigate the SageMaker Studio UI.

Demo 1: SageMaker UI Walkthrough

Lab 1: Launch SageMaker Studio from AWS Service Catalog

• Use Amazon SageMaker Studio to collect, clean, visualize, analyze, and transform data.
• Set up a repeatable process for data processing.
• Use SageMaker to validate that collected data is ML ready.
• Detect bias in collected data and estimate baseline model accuracy.

Lab 2: Analyze and Prepare Data Using SageMaker Data Wrangler

Lab 3: Analyze and Prepare Data at Scale Using Amazon EMR

Lab 4: Data Processing Using SageMaker Processing and the SageMaker Python SDK

Lab 5: Feature Engineering Using SageMaker Feature Store

• Use Amazon SageMaker Studio to develop, tune, and evaluate an ML model against business objectives and fairness and explainability best practices.
• Fine-tune ML models using automatic hyperparameter optimization capability.
• Use SageMaker Debugger to surface issues during model development.

Demo 2: Autopilot

Lab 6: Track Iterations of Training and Tuning Models Using SageMaker Experiments

Lab 7: Analyze, Detect, and Set Alerts Using SageMaker Debugger

Lab 8: Identify Bias Using SageMaker Clarify

• Use Model Registry to create a model group; register, view, and manage model versions; modify model approval status; and deploy a model.
• Design and implement a deployment solution that meets inference use case requirements.
• Create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines.

Lab 9: Inferencing with SageMaker Studio

Lab 10: Using SageMaker Pipelines and the SageMaker Model Registry with SageMaker Studio

• Configure a SageMaker Model Monitor solution to detect issues and initiate alerts for changes in data quality, model quality, bias drift, and feature attribution (explainability) drift.
• Create a monitoring schedule with a predefined interval.

Demo 3: Model Monitoring

• List resources that accrue charges.
• Recall when to shut down instances.
• Explain how to shut down instances, notebooks, terminals, and kernels.
• Understand the process to update SageMaker Studio.

• The Capstone lab will bring together the various capabilities of SageMaker Studio discussed in this course.
• Students will be given the opportunity to prepare, build, train, and deploy a model using a tabular dataset not seen in earlier labs.
• Students can choose among basic, intermediate, and advanced versions of the instructions.

Capstone Lab: Build an End-to-End Tabular Data ML Project Using SageMaker Studio and the SageMaker Python SDK