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Deploying Models to AWS Endpoints with SageMaker

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Overview

Take your models live with SageMaker’s powerful deployment options. Learn to package and deploy models to serverless and real-time endpoints, test predictions at scale, and manage the full deployment lifecycle. You’ll gain hands-on experience with endpoint monitoring, cleanup, and cost management for reliable, production-ready inference.

Syllabus

Unit 1: Deploying Locally Trained Models to SageMaker

Package Model for SageMaker Deployment
Create SageMaker Model Object Blueprint
Deploy Serverless Model to SageMaker Endpoint
Connect to Your Deployed SageMaker Endpoint
Test Your Deployed Model Performance

Unit 2: Serving Estimator Models with Serverless Endpoints

Finding Latest Estimator Training Job
Attaching to Existing Training Jobs
Deploying Estimator Models to Serverless Endpoints
Testing Deployed Estimator Endpoint

Unit 3: Publishing ModelTrainer Models to SageMaker Endpoints

Finding ModelTrainer Jobs and Artifacts
Configuring SKLearnModel for ModelTrainer Artifacts
Deploying ModelTrainer Models to Serverless Endpoints
Testing Your ModelTrainer Endpoint Performance

Unit 4: Real-Time Endpoint Deployment

Deploy Your First Real Time Endpoint
High Performance Multi Instance Deployment
Transform Serverless to Real Time Deployment
Performance Comparison Between Endpoint Types

Unit 5: Managing and Cleaning Up Endpoints

Check Your Deployed Endpoint Status
Inspect Endpoint Configuration Details
Bulk Cleanup of All Endpoints
Manage Endpoints Using AWS CLI

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