ml-deployment-helper
Prepares ML models for production deployment with containerization, API creation, monitoring setup, and A/B testing. Activates for "deploy model", "production deployment", "model API", "containerize model", "docker ml", "serving ml model", "model monitoring", "A/B test model". Generates deployment artifacts and ensures models are production-ready with monitoring, versioning, and rollback capabilities.
npx skills add majiayu000/claude-skill-registry --skill ml-deployment-helper --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML Deployment Helper ## Overview Bridges the gap between trained models and production systems. Generates deployment artifacts, APIs, monitoring, and A/B testing infrastructure following MLOps best practices. ## Deployment Checklist Before deploying any model, this skill ensures: - ✅ Model versioned and tracked - ✅ Dependencies documented (requirements.txt/Dockerfile) - ✅ API endpoint created - ✅ Input validation implemented - ✅ Monitoring configured - ✅ A/B testing ready - ✅ Rollback plan documented - ✅ Performance benchmarked ## Deployment Patterns ### Pattern 1: REST API (FastAPI) ```python from specweave import create_model_api # Generates production-ready API api = create_model_api( model_path="models/model-v3.pkl", increment="0042", framework="fastapi" ) # Creates: # - api/ # ├── main.py (FastAPI app) # ├── models.py (Pydantic schemas) # ├── predict.py (Prediction logic) # ├── Dockerfile # ├── requirements.txt # └── tests/ ``` Generated `main.py`: ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel import joblib app = FastAPI(title="Recommendation Model API", version="0042-v3") model = joblib.load("model-v3.pkl") class PredictionRequest(BaseM
- Overview
- Deployment Checklist
- Deployment Patterns
- Pattern 1: REST API (FastAPI)
- Pattern 2: Batch Prediction
- Pattern 3: Real-Time Streaming
- Containerization
- Monitoring Setup
- A/B Testing Infrastructure
- Model Versioning
- Load Testing
- Deployment Commands
- Deployment Increment
- Best Practices
Generate deployment artifacts Create API Setup monitoring Create A/B test Load test Deploy to production After training model (increment 0042) Generates deployment increment with all artifacts Deploy to production when ready
What does the ml-deployment-helper skill do?
Prepares ML models for production deployment with containerization, API creation, monitoring setup, and A/B testing. Activates for "deploy model", "production deployment", "model API", "containerize model", "docker ml", "serving ml model", "model monitoring", "A/B test model". Generates deployment artifacts and ensures models are production-ready with monitoring, versioning, and rollback capabilities.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-deployment-helper --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.