model-deployment

Solid

Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.

DevOps & Infrastructure 168 stars 27 forks Updated 4 weeks ago MIT

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Quality Score: 89/100

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Description 5%
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Skill Content

# ML Model Deployment Deploy trained models to production with proper serving and monitoring. ## Deployment Options | Method | Use Case | Latency | |--------|----------|---------| | REST API | Web services | Medium | | Batch | Large-scale processing | N/A | | Streaming | Real-time | Low | | Edge | On-device | Very low | ## FastAPI Model Server ```python from fastapi import FastAPI from pydantic import BaseModel import joblib import numpy as np app = FastAPI() model = joblib.load('model.pkl') class PredictionRequest(BaseModel): features: list[float] class PredictionResponse(BaseModel): prediction: float probability: float @app.get('/health') def health(): return {'status': 'healthy'} @app.post('/predict', response_model=PredictionResponse) def predict(request: PredictionRequest): features = np.array(request.features).reshape(1, -1) prediction = model.predict(features)[0] probability = model.predict_proba(features)[0].max() return PredictionResponse(prediction=prediction, probability=probability) ``` ## Docker Deployment ```dockerfile FROM python:3.11-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY model.pkl . COPY app.py . EXPOSE 8000 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"] ``` ## Model Monitoring ```python class ModelMonitor: def __init__(self): self.predictions = [] self.latencies = [] def log_prediction(self, input_data, predi...

Details

Author
secondsky
Repository
secondsky/claude-skills
Created
7 months ago
Last Updated
4 weeks ago
Language
TypeScript
License
MIT

Integrates with

Cloudflare · Cloud Docker · Infrastructure Kubernetes · Infrastructure FastAPI · Backend REST API · API

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