Amazon Web Services (AWS) - SageMaker Studio for Data Scientists

Curriculum

Module 1: Amazon SageMaker Setup and Navigation

• 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

Module 2: Data Processing 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

Module 3: Model Development 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

Module 4: Deployment and Inference 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

Module 5: Monitoring 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

Module 6: Managing SageMaker Studio Resources and Updates 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.
• Capstone:

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.