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HarrypatriaHarrypatria/data-mining-ml/skills/model-deployment/SKILL.md

model-deployment

Skill2 starsChanged 12 days agoMITView on GitHub

Model Deployment. Serve a trained model in production - load a pickled pipeline safely, FastAPI prediction service (predict, batch, what-if, SHAP explanations, LLM narrative), Pydantic validation from schema.json, Docker multi-stage image, docker-compose with nginx, health checks, API keys, CORS, rate limits, JSON logging, Prometheus metrics, drift monitoring (PSI, KS), batch scoring, ONNX or PMML export and air-gapped installs. Use for deploy, serve, productionize, REST API, MLOps, model serving, monitoring or containerizing a model.

4 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Verify the bundle: bin/data-mining-ml verify-model models/<version>.

    line 12
  2. Very specific:

    Run tests: cd api && pytest (schema validation, predict, what-if additivity, explanations, narrative with the provider mocked, grounding, …

    line 14
  3. Very specific:

    Run locally: uvicorn app.main:create_app --factory --port 8000 with MODEL_DIR=../models APP_ENV=development.

    line 15

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Model Deployment

Scaffold: templates/ml-service/ (becomes api/ via bin/data-mining-ml init). The pickled pipeline from model-training is the model of record.

Steps

  1. Verify the bundle: bin/data-mining-ml verify-model models/<version>.
  2. Copy the scaffold, install src/<package> into the image, pin api/requirements.txt to metadata.json libraries.
  3. Run tests: cd api && pytest (schema validation, predict, what-if additivity, explanations, narrative with the provider mocked, grounding, security limits, tamper detection).
  4. Run locally: uvicorn app.main:create_app --factory --port 8000 with MODEL_DIR=../models APP_ENV=development.
  5. Build and run: docker compose up --build (API plus nginx serving the React app; nginx injects the API key).
  6. Wire monitoring: scrape /metrics; alert on feature_drift_psi above 0.2, rising input_range_violations_total, and explainer fallbacks.

Endpoints

RoutePurpose
GET /health/live, GET /health/readyLiveness; readiness means loaded and verified
GET /v1/model, GET /v1/schemaVersion, metrics with intervals, threshold, limits; predictor definitions for the UI
POST /v1/predict, POST /v1/predict/batchProbability or value, label at the stored threshold, version, request id, extrapolation flags
POST /v1/whatifBaseline versus scenario, delta, contributions for both
POST /v1/explain/local, POST /v1/explain/counterfactualsShapley contributions; smallest threshold-crossing changes
POST /v1/explain/narrativeServer-built RTCC context, LLM answer, grounding check, SSE stream

Engineering rules

  • Load once in the lifespan handler; keep it on app.state; never load inside a route.
  • Inference is blocking: plain def routes (FastAPI threadpool) or run_in_threadpool.
  • Pydantic models generated from schema.json; unknown fields rejected; categories enumerated; out-of-range numerics need allow_extrapolation and come back flagged.
  • Column order and dtypes come from the schema, enforced before predict.
  • The server recomputes everything it sends to the LLM; clients never send explanation values.
  • API key dependency, explicit CORS allowlist without credentials, body size limit, rate limit, no upload endpoint.
  • JSON logs with request id and no raw personal data; /metrics with request counts, latency, prediction distribution, range violations and PSI drift.
  • Docker: multi-stage, slim base, pinned requirements, non-root user, no --reload, HEALTHCHECK, model directory mounted read-only. Each worker holds its own model copy; size workers to memory.
  • Air-gapped: vendored wheels (pip download then --no-index --find-links), LLM_PROVIDER=none makes no external calls and uses template explanations.

Details, batch scoring, ONNX and PMML, Java and .NET: references/fastapi-serving.md.

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