- Purpose: After training a machine learning model on SageMaker, you deploy it to an endpoint to make real-time predictions. SageMaker endpoints provide a managed hosting service for your models.
- API Gateway: Endpoints expose an HTTPS API that applications can call to receive predictions based on the deployed model.
Configuration
- Endpoint Configuration:
- Specify the trained model(s) to associate with the endpoint.
- Choose the instance types and number of instances for hosting.
- Production Variants (Optional):
- Deploy multiple models or model versions behind a single endpoint.
- Distribute traffic with different weights to each variant, enabling A/B testing or gradual rollout of updates.
Best Practices
- Scaling:
- Configure autoscaling to match resource demands and prevent bottlenecks or over-provisioning.
- Consider multi-AZ deployment for high availability.
- Monitoring:
- Set up CloudWatch metrics and logs to track API latency, invocations, errors, and resource utilization.
- Versioning:
- Maintain clear versioning for models deployed to endpoints to enable rollbacks and audit trails.
- Security:
- Use IAM roles to control access to the endpoint.
- Consider network isolation or VPC integration for sensitive use cases.
Solid Use Cases
- Real-time Fraud Detection: Making predictions on transactions to identify potential fraud as they occur.
- Image Classification: Deploying image classification models for online applications or mobile apps.
- Computer Vision in Manufacturing: Predicting defects or anomalies in products in real-time using computer vision models.
- Recommender Systems: Providing personalized product or content recommendations for users.
- Natural Language Processing Tasks: Sentiment analysis, machine translation, or text classification on live data streams.