model-serving

Name: model-serving
Availability: InStock
Author: plugin

Deploy ML models for production inference with BentoML and Triton.

1|Updated Nov 18, 2025

Search Tags:#deploy #scaling #inference #triton #model-serving #bentoml

Ready to run

npx skills add https://github.com/pluginagentmarketplace/custom-plugin-mlops --skill model-serving-pluginagentmarketplace

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: model-serving
Source: https://github.com/pluginagentmarketplace/custom-plugin-mlops/tree/main/skills/model-serving
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-mlops --skill model-serving-pluginagentmarketplace

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of deploying ML models for production inference at scale with optimized latency and resource utilization.

Core Features & Use Cases

Automated deployment of ML models to cloud, on-prem, and edge targets.
Inference optimization using quantization and hardware acceleration.
Auto-scaling and monitoring to maintain latency budgets.
Edge deployment support and CI/CD integration.

Quick Start

Install BentoML/Triton, prepare a trained model, and run the deployment workflow using the provided scripts.

Frequently Asked Questions about model-serving

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

FAQPage Schema

How do I deploy ML models for production inference with BentoML or Triton?▼

You can deploy ML models for production inference by installing BentoML or Triton, preparing your trained model, and running the provided deployment workflow scripts. This enables automated deployment to cloud, on-premises, or edge targets with optimized performance.

What is the best way to configure autoscaling for deployed ML models?▼

Configuring autoscaling for deployed ML models is handled through the Skill's deployment scripts, which set up auto-scaling rules and basic monitoring integration to maintain latency budgets and ensure resource utilization adapts to inference demand.

Can I optimize inference latency using quantization and hardware acceleration?▼

Yes, you can optimize inference latency using quantization and hardware acceleration. The Skill provides inference optimization capabilities specifically designed to reduce latency and improve resource utilization during production model serving.

Does model deployment work for both cloud and edge targets?▼

Yes, model deployment works for both cloud and edge targets. The Skill supports automated deployment of ML models across cloud, on-premises, and edge environments, ensuring inference capabilities are available across diverse infrastructure setups.

Do I need yaml to run the model serving deployment workflow?▼

Yes, you need yaml to run the model serving deployment workflow. Yaml is listed as a required dependency to configure and execute the scripts that deploy ML models for production inference.

Why use BentoML vs Triton for ML model serving?▼

Using BentoML vs Triton for ML model serving depends on your deployment target. Both are supported by the Skill to handle automated deployment, inference optimization, and scaling, allowing you to choose the framework that best fits your infrastructure.