Model Serving

Name: Model Serving
Availability: InStock
Author: doanchienthangdev

Deploy machine learning models with TensorFlow Serving, TorchServe, or Triton Inference Server.

4|1|Updated Dec 30, 2025

Search Tags:#deployment #mlops #kubernetes #model serving #torchserve #tensorflow serving #triton inference server

Ready to run

npx skills add https://github.com/doanchienthangdev/omgkit --skill model-serving-doanchienthangdev

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Model Serving
Source: https://github.com/doanchienthangdev/omgkit/tree/main/plugin/skills/ai-ml/model-serving
Command: npx skills add https://github.com/doanchienthangdev/omgkit --skill model-serving-doanchienthangdev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of deploying machine learning models into production environments, ensuring they are accessible for real-time or batch predictions with high performance and reliability.

Core Features & Use Cases

Model Deployment: Provides guidance and code examples for deploying models using TensorFlow Serving, TorchServe, and Triton Inference Server.
Optimization Techniques: Covers essential model optimization methods like quantization and ONNX export for improved inference speed and reduced model size.
Orchestration: Includes Kubernetes deployment manifests for scalable and resilient model serving infrastructure.
Use Case: Deploy a trained image classification model using Triton Inference Server on Kubernetes, enabling a web application to receive real-time image analysis results.

Quick Start

Deploy the 'image_classifier' model using Triton Inference Server with the provided Kubernetes manifests.

Frequently Asked Questions about Model Serving

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

FAQPage Schema

How do I deploy machine learning models to production using Kubernetes?▼

To deploy machine learning models to production, you can use Kubernetes orchestration manifests to set up scalable and resilient serving infrastructure with servers like Triton, TorchServe, or TensorFlow Serving.

What is the best way to serve a trained image classification model in real-time?▼

The best way to serve a trained image classification model in real-time is deploying it with Triton Inference Server on Kubernetes, enabling web applications to receive immediate analysis results.

Does this approach support model optimization techniques like quantization and ONNX export?▼

Yes, the model serving workflow supports essential optimization techniques including quantization and ONNX export to improve inference speed and reduce model size before deployment.

Can I use TorchServe and TensorFlow Serving for scalable model serving?▼

Yes, you can use TorchServe and TensorFlow Serving for scalable model serving, as the infrastructure setup covers both platforms along with Triton Inference Server for production environments.

When do I need ONNX export for deploying ML models?▼

You need ONNX export for deploying ML models when you require improved inference speed and reduced model size, allowing optimized models to run efficiently across different serving frameworks.