What ML frameworks does this skill support?

The skill supports major machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, and XGBoost, providing specific deployment and serving patterns for each.

Does it include infrastructure best practices?

Yes, it offers production patterns for containerization with Docker, orchestration with Kubernetes, and deployment on major cloud platforms like AWS, GCP, and Azure.

How does it handle MLOps and monitoring?

It provides guidance on setting up drift detection, model versioning, and experiment tracking using industry-standard tools like MLflow, Weights & Biases, and Evidently.

Can this skill help with Large Language Model (LLM) integration?

Yes, it includes specialized patterns for LLM integration, including RAG (Retrieval-Augmented Generation) systems, vector database management, and prompt engineering with DSPy.

ML Engineering & MLOps

Name: ML Engineering & MLOps
Author: eyadsibai

byeyadsibai

数据科学与机器学习

Deploys production-grade machine learning models and implements robust MLOps infrastructure for scalable AI systems.

关于

This skill provides Claude with domain-specific expertise for building, deploying, and maintaining production-level machine learning and AI systems. It covers the entire lifecycle from model serving with FastAPI and feature store integration to advanced monitoring for data drift and performance. It is particularly useful for engineers building RAG systems, managing LLM integrations, or setting up automated retraining pipelines using modern tools like PyTorch, LangChain, and MLflow.

主要功能

Production-grade model deployment patterns for PyTorch and TensorFlow
Advanced RAG system architectures and prompt management with DSPy
0 GitHub stars
Model monitoring and drift detection using MLflow and Evidently
End-to-end MLOps pipeline implementation including automated retraining
Standardized infrastructure setup for Docker and Kubernetes

使用场景

Deploying a high-throughput PyTorch model as a containerized FastAPI service
Setting up automated monitoring and drift detection for production models
Implementing a RAG-based LLM application with Pinecone vector storage

关于

主要功能

Production-grade model deployment patterns for PyTorch and TensorFlow
Advanced RAG system architectures and prompt management with DSPy
0 GitHub stars
Model monitoring and drift detection using MLflow and Evidently
End-to-end MLOps pipeline implementation including automated retraining
Standardized infrastructure setup for Docker and Kubernetes

使用场景

Deploying a high-throughput PyTorch model as a containerized FastAPI service
Setting up automated monitoring and drift detection for production models
Implementing a RAG-based LLM application with Pinecone vector storage