Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.
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Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.
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</> SKILL.md
model-deployment.SKILL.md
---name: model-deployment
description: Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.
license: MIT
metadata:
keywords: "model deployment, FastAPI, Docker, Kubernetes, ML serving, model monitoring, drift detection, A/B testing, CI/CD, mlops, production ml, model versioning, health checks, Prometheus, containerization, rolling updates, blue-green deployment, canary deployment, model registry"
---# 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):
- **Test before deploy**: Unit tests, integration tests, performance benchmarks
- **Deploy gradually**: Canary (10%), then full rollout
- **Plan for rollback**: Keep previous version, document procedure
- **Log predictions**: Enable debugging and drift detection
- **Set resource limits**: Prevent OOM kills and resource contention
- **Use health checks**: Enable proper load balancing
## When to Load References
Load reference files for detailed implementations:
- **FastAPI Production Server**: Load `references/fastapi-production-server.md` for complete production-ready FastAPI implementation with error handling, validation (Pydantic models), logging, health/readiness probes, batch predictions, model versioning, middleware, exception handlers, and performance optimizations (caching, async)
- **Model Monitoring & Drift**: Load `references/model-monitoring-drift.md` for ModelMonitor implementation with KS-test drift detection, Jensen-Shannon divergence, Prometheus metrics integration, alert configuration (Slack, email), continuous monitoring service, and dashboard endpoints
- **Containerization & Deployment**: Load `references/containerization-deployment.md` for multi-stage Dockerfiles, model versioning in containers, Docker Compose setup, A/B testing with Nginx, Kubernetes deployments (rolling update, blue-green, canary), GitHub Actions CI/CD, and deployment checklists
- **CI/CD for ML Models**: Load `references/cicd-ml-models.md` for complete GitHub Actions pipeline with model validation, data validation, automated testing, security scanning, performance benchmarks, automated rollback, and deployment strategies
