Integration patterns with Amazon SageMaker

Typical Usage Pattern:

1. Data scientists develop and test models in Jupyter notebooks

2. Once the model is ready, the workflow is converted to a Step Functions state machine

3. Step Functions handles the production deployment and retraining

4. Notebooks remain useful for ad-hoc analysis and investigation

An example of end-to-end AWS service integration patterns with Amazon SageMaker as the central ML service is:

I've created a diagram showing the key integration patterns for Amazon SageMaker. Here's a breakdown of the main components and flows:

1. Data Sources:

• S3 Data Lakes for unstructured data

• RDS and RedShift for structured data

• DynamoDB for NoSQL data

• Kinesis for real-time streaming data

1. Data Processing Layer:

• AWS Glue for ETL operations

• EMR for big data processing

• Lambda for serverless transformations

1. SageMaker Core Components:

• SageMaker Studio for development environment

• Model Training for building ML models

• Data Processing for feature engineering

• Hyperparameter Optimization for model tuning

• SageMaker Pipeline for ML workflows

• Model Registry for versioning

• Model Endpoints for deployment

1. Deployment & Monitoring:

• CloudWatch for metrics and logging

• EventBridge for event orchestration

• Step Functions for workflow automation

1. Application Integration:

• API Gateway for RESTful interfaces

• App Runner for containerized applications

• ECS/EKS for container orchestration