domino-model-endpoints

Name: domino-model-endpoints
Availability: InStock
Author: Domino Data Lab

Deploy, monitor, and scale ML models as API endpoints in Domino Data Lab.

6|3|Updated Feb 19, 2026

Search Tags:#monitoring #api #mlops #scaling #model deployment #domino

Ready to run

npx skills add https://github.com/dominodatalab/domino-claude-plugin --skill domino-model-endpoints-dominodatalab

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: domino-model-endpoints
Source: https://github.com/dominodatalab/domino-claude-plugin/tree/main/skills/model-endpoints
Command: npx skills add https://github.com/dominodatalab/domino-claude-plugin --skill domino-model-endpoints-dominodatalab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of deploying machine learning models as production-ready API endpoints, enabling seamless integration into applications and workflows.

Core Features & Use Cases

Model Deployment: Easily deploy trained models from your Domino projects as scalable REST APIs.
Monitoring & Scaling: Provides tools for monitoring endpoint performance (latency, errors, resource usage) and automatically scales resources based on demand.
Use Case: Deploy a fraud detection model as an API that your e-commerce platform can call in real-time to score transactions.

Quick Start

Use the domino-model-endpoints skill to deploy the 'fraud-detector' model from your current project.

Frequently Asked Questions about domino-model-endpoints

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I deploy machine learning models as REST APIs in Domino?▼

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.

Can I monitor API endpoint latency and errors for deployed models?▼

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.

Does Domino support GPU inference for scaling model deployment?▼

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.

What is the best way to scale model endpoints for real-time applications?▼

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.

How does version management work for Domino model APIs?▼

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.

When do I need to deploy a model as an API endpoint instead of running batch jobs?▼

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.