domino-model-endpoints

To deploy machine learning models as REST APIs in Domino, you use the model endpoint feature to expose your trained project files as scalable APIs. This allows seamless integration of your models into external applications and workflows.

You can monitor API endpoint latency and errors using integrated Grafana dashboards. This provides real-time tracking of performance metrics, resource usage, and scaling behavior to ensure production-grade model serving reliability.

Domino supports GPU inference for scaling model deployment via NVIDIA Triton integration. This enables high-performance hardware acceleration to handle increased demand and optimize resource usage during API requests.

The best way to scale model endpoints for real-time applications is configuring automatic resource scaling based on demand. This ensures your API maintains low latency and high availability during traffic spikes without manual intervention.

Version management for Domino model APIs allows you to track and update deployed endpoints throughout their lifecycle. You can maintain multiple iterations of a model to ensure consistent API behavior while deploying new versions safely.

You need to deploy a model as an API endpoint when real-time scoring is required, such as calling a fraud detection model during an e-commerce transaction. API endpoints provide immediate responses for live application integration.