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Model Deployment

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Deploy machine learning models to production using Flask, FastAPI, Docker, cloud platforms (AWS, GCP, Azure), and model serving frameworks

skills/model-deployment/SKILL.md

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# Model Deployment

## Overview

Model deployment is the process of taking a trained machine learning model and making it available for production use through APIs, web services, or batch processing systems.

## When to Use

- When productionizing trained models for real-world inference and predictions
- When building REST APIs or web services for model serving
- When scaling predictions to serve multiple users or applications
- When deploying models to cloud platforms, edge devices, or containers
- When implementing CI/CD pipelines for ML model updates
- When creating batch processing systems for large-scale predictions
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