Amazon SageMaker Studio for Data Scientists

Course subjects

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, visualise, analyse, 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: Analyse and Prepare Data Using SageMaker Data Wrangler

• Lab 3: Analyse 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 optimisation 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: Analyse, 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

Please note: This is an emerging technology course. Course outline is subject to change as needed.