Live-Online: Amazon SageMaker Studio for Data Scientists

This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.

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. You will be given the opportunity to prepare, build, train, and deploy a model using a tabular dataset not seen in earlier labs. You 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

• Accelerating the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
• Using the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle

This course is intended for the following job roles:

• Machine Learning & AI

The following course or equivalent knowledge is required: MLOps Engineering on AWS