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Training Models in AWS with SageMaker Python SDK

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Overview

Unlock the power of cloud-based machine learning with Amazon SageMaker. Set up your AWS environment, upload data to S3, and launch scalable training jobs using the SageMaker Python SDK. You’ll learn how to monitor jobs, retrieve trained models, and evaluate results—building a strong foundation for advanced SageMaker workflows.

Syllabus

Unit 1: Getting Started with Amazon SageMaker

Quiz on Amazon SageMaker

Unit 2: Moving Your Data to the Cloud

Getting Your Default Bucket
Uploading Your First Dataset to S3
Organizing Data with Nested S3 Prefixes
Verifying Data Integrity After Upload

Unit 3: Training Models with SageMaker Estimators

Building the S3 Data URI
Setting Up SageMaker Authentication Roles
Configuring Compute Resources and Storage Paths
Creating and Configuring the SKLearn Estimator
Launching Your First Cloud Training Job

Unit 4: Retrieving and Evaluating Trained Models

Retrieving Latest Estimator Training Job
Connecting to Completed Training Jobs
Downloading and Loading Trained Models
Evaluating Model Performance on Test Data
Making Single House Price Predictions

Unit 5: Advanced Training with ModelTrainer

Retrieving Container Images with ModelTrainer
Configuring ModelTrainer with Modular Objects
Launching Your First ModelTrainer Job
Monitoring Your ModelTrainer Job Progress
Retrieving and Evaluating Completed ModelTrainer Jobs

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