ml-engineering

What problem does it solve?

This Skill provides guidance and tools for deploying, managing, and monitoring Machine Learning models in production environments, addressing the complexities of MLOps.

Core Features & Use Cases

• Model Deployment: Strategies and code examples for serving ML models using frameworks like FastAPI and Docker.
• MLOps Pipelines: Best practices for building automated pipelines for training, deployment, and monitoring.
• LLM Integration: Patterns for integrating Large Language Models into production systems, including RAG and prompt management.
• Use Case: You need to deploy a trained PyTorch model as a REST API. This Skill can provide the FastAPI code structure, Dockerfile, and deployment commands.

Quick Start

Use the ml-engineering skill to generate a FastAPI application for serving a PyTorch model.