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Found 2 Skills
Deploy ML models with FastAPI, Docker, Kubernetes. Use for serving predictions, containerization, monitoring, drift detection, or encountering latency issues, health check failures, version conflicts.
Use the Geti application end to end through its REST API — the project → dataset → annotate → train → deploy pipeline served by the FastAPI backend in `application/backend/`. Use when a user (not a contributor) wants to create a project, upload media, add annotations, launch a training or quantization job, track job status, configure a source → model → sink inference pipeline, and enable live inference. Covers the `/api/...` endpoints and the async job model, not backend code changes.