ml-production-ecosystem 0.1.0

Shared MLOps learning architecture for production ML engineering stages.

ML Production Ecosystem

Published Python generator for local-first ML project scaffolds, plus repository-only lifecycle examples for production ML engineering.

Generate a served model

Prerequisites: Python 3.11+ and uv. Docker is required only for the container step.

From any directory outside this checkout:

uvx --refresh --from ml-production-ecosystem==0.1.0 create-ml-struct churn-api --preset served-model --no-input

That single command only generates the project. Testing, training, serving, prediction, and container building remain explicit commands inside the generated project:

cd churn-api
uv run pytest
uv run python -m churn_api.train
PORT=18080 uv run serve

In another terminal:

curl http://127.0.0.1:18080/health
curl -X POST http://127.0.0.1:18080/predict \
  -H "Content-Type: application/json" \
  -d '{"features":{"a":1.0,"b":2.0}}'
docker build -t churn-api .

The default served-model preset resolves training, evaluation, api, deployment, quality-gate, monitoring, docker, and ci.

flowchart LR
    A[PyPI package] --> B[create-ml-struct]
    B --> C[generated project]
    C --> D[tests and train]
    D --> E[FastAPI health and predict]
    E --> F[Docker image]

Generated served-model tree

churn-api/
├── .github/workflows/ci.yml
├── Dockerfile
├── README.md
├── configs/project.yaml
├── data/README.md
├── docs/
│   ├── infra-checklist.md
│   └── runbook.md
├── ml-struct.yaml
├── orchestration/monitoring_job.py
├── pyproject.toml
├── tests/
│   ├── test_deployment.py
│   ├── test_evaluate.py
│   ├── test_scaffold.py
│   └── test_training.py
└── churn_api/
    ├── __init__.py
    ├── api.py
    ├── deployment.py
    ├── evaluate.py
    ├── predict.py
    └── train.py

After training, these evidence files are created:

artifacts/reports/training-summary.json
artifacts/reports/metrics.json

The starter contract is intentionally honest: training writes deterministic evidence only. /predict remains a dummy threshold seam and does not load the training artifact. Replace churn_api/predict.py and its API contract when integrating a real model.

Other presets

uvx --from ml-production-ecosystem==0.1.0 create-ml-struct --list-presets

Available presets: kaggle, generic-classifier, served-model, asr-served-model, recommendation, batch-inference, existing-model-wrapper, llm-post-training, and enterprise-pipeline.

The full wizard remains available as ml-struct or mle. It can select task, model type, backend, provider, and repeatable infrastructure components. Component dependencies are auto-added and recorded in ml-struct.yaml.

Repository-only development

Clone the repository only to work on the generator or run its broader lifecycle examples:

cd ml-production-ecosystem
uv run ml-struct doctor
uv run ml-struct quickstart
./scripts/validate-production-patterns.sh
./scripts/validate-served-model-release.sh

Area	Path	Purpose
Runtime package	src/ml_production_ecosystem/	Importable generator and local MLOps examples
Scaffold templates	src/ml_production_ecosystem/templates/scaffold/	Packaged template resources used by source, wheel, and sdist installs
Examples	examples/samples/	Repository-only runnable samples
Runtime state	artifacts/, logs/, registry/	Generated local evidence outside source code
Configs	configs/	Repository lifecycle and provider-boundary examples

The examples are model-agnostic and provider-agnostic: model and cloud details belong behind explicit contracts and adapters.

The repository is local-first. It does not claim managed cloud deployment, a real Kubernetes runtime, production paging, or automatic use of a trained artifact by generated serving code.