Agent skill · software engineering · secondsky
model-deployment
Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.
Why this skill is useful
Adds executable scripts for deploying ML models with FastAPI and Docker, including monitoring and health checks that are not commonly found in public documentation.
What it needs
Requires docker, kubectl installed locally. About 4k tokens when loaded. Last updated 2026-08-06. 204 stars on the source repository.
What this skill does
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 Docker Deployment Model Monitoring Deployment Checklist [ ] Model validated on test set [ ] API endpoints documented [ ] Health check endpoint [ ] Authentication configured [ ] Logging and monitoring setup [ ] Model versioning in place [ ] Rollback procedure documented Quick Start: Deploy Model in 6 Steps Known Issues Prevention 1. No Health Checks = Downtime Problem: Load balancer sends traffic to unhealthy pods, causing 503 errors. Solution: Implement both liveness and readiness probes: 2. Model Not Found Errors in Container Problem: FileNotFoundError: model.pkl when container starts. Solution: Verify model file is copied in Dockerfile and path matches: 3. Unhandled Input Validation = 500 Errors Problem: Invalid inputs crash API with unhandled exceptions. Solution: Use Pydantic for automatic validation: 4. No Drift Monitoring = Silent Degradation Problem: Model performance degrades over time, no one notices until users complain. Solution: Implement drift detection (see references/model-monitoring-drift.md): 5. Missing Resource Limits = OOM Kills Problem: Pod killed by Kubernetes OOMKiller, service goes down. Solution: Set memory/CPU limits and requests: 6. No Rollback Plan = Stuck on Bad Deploy Problem: New model version has bugs, no way to revert quickly. Solution: Tag images with versions, keep previous deployment: 7. Synchronous Prediction = Slow Batch Processing Problem: Processing 10,000 predictions one-by-one takes hours. Solution: Implement batch endpoint: 8. No CI/CD Validation = Deploy Bad Models Problem: Deploying model that fails basic tests, breaking production. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills secondsky/model-deployment