Course Overview

The Amazon SageMaker Studio for Data Scientists course provides hands-on experience with data processing, model development, and deployment using Amazon SageMaker Studio. Participants will learn to clean and prepare data, develop machine learning models, and manage end-to-end ML workflows. The course also covers model monitoring and managing resources in SageMaker. This Amazon SageMaker Studio Training for Data Scientists equips professionals with essential skills for building scalable ML solutions.

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Level: Advanced

Duration: 3 Days

Delivery Type: Instructor-Led Training

Course Objectives

Accelerate the preparation, building, training, deployment, and monitoring of machine learning solutions by using Amazon SageMaker Studio.

Prerequisites

Recommended

AWS Technical Essentials

Who Should Go For This Training

Data Scientist

Course Outline

Module 1: Setup and SageMaker Navigation

Launch SageMaker Studio from the Service Catalog

Navigate the SageMaker Studio UI

Demo 1: SageMaker UI Walkthrough

Demo 2: Creating EMR cluster in SageMaker UI

Lab 1: Setting Up Amazon SageMaker Studio

Module 2: Data Processing

Use SageMaker Studio to collect, clean, visualize, analyze, and transform data

Set up a repeatable process for data processing

Use SageMaker to validate collected data is ML-ready

Detect bias in collected data and estimate baseline model accuracy

Lab 2: Analyze and Prepare Data Using Amazon SageMaker Data Wrangler

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

Lab 4: Data Processing Using Amazon SageMaker Processing and Sagemaker Python SDK

Lab 5: Feature Engineering Using SageMaker Feature Store

Module 3: Model Development

Use SageMaker Studio to develop, tune, and evaluate a machine learning model against business objectives and fairness and explainability best practices

Fine-tune machine learning models using automatic hyperparameter optimization capability

Use debugger to surface issues during model development

Demo 3: Algorithms (Notebooks)

Demo 4: Debugging

Demo 5: Autopilot

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

Lab 7: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger

Lab 8: Using SageMaker Clarify for Bias, and Explainability

Module 4: Deployment and Inference

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

Use Model Registry to create a Model Group, register, view, and manage model versions, modify model approval status and deploy a model

Lab 9: Inferencing with SageMaker Studio

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

Module 5: Monitoring

Configure a Model Monitor solution to detect issues and initiate alerts for changes in data quality, model quality, bias, and feature attribution drift

Create a monitoring schedule with a predefined interval

Demo 6: 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

Module 7: 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