Amazon SageMaker Studio for Data Scientists

COURSE OUTLINE

Day 1 

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