godml 1.1.1

Governed, Observable & Declarative Machine Learning Framework

GODML

Governed, Observable & Declarative Machine Learning Framework

Production-grade MLOps for teams that need traceability, compliance, and a verified supply chain — without the infrastructure overhead.

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Quick start

pip install godml
godml init my-project
godml run -f godml.yml

That's it. No cloud account required for local training.

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What is GODML?

GODML is a Python framework that wraps the full ML lifecycle — data prep, training, evaluation, monitoring, and deployment — behind a single declarative YAML config. Every run produces a signed, auditable artifact trail.

Raw data → Compliance check → Train → Evaluate → Registry → Deploy → Monitor
               (PII/GDPR)    (XGB/RF/LR)  (cross-val)  (MLflow)  (Docker)  (drift)

Why GODML over plain sklearn + MLflow?

Problem	Without GODML	With GODML
Reproducibility	Manual notebooks	Declarative YAML, locked hashes
Compliance	Ad-hoc checks	Built-in PCI-DSS, GDPR, HIPAA
Supply chain	No SBOM	SLSA L3 provenance + signed SBOM
Audit trail	Scattered logs	Unified lineage per run
Multi-model	Custom glue code	Registry + notebook_api

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Installation

Core (no optional deps)

pip install godml

With extras

pip install "godml[advisor]"   # LLM-powered recommendations (gpt4all)
pip install "godml[deep]"      # LSTM forecasting (tensorflow + keras)
pip install "godml[aws]"       # SageMaker deployment
pip install "godml[api]"       # REST inference server (fastapi + uvicorn)
pip install "godml[dev]"       # Full dev suite (tests, lint, coverage)

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Configuration

A minimal godml.yml:

name: customer-churn
version: 1.0.0
provider: mlflow

dataset:
  uri: ./data/churn.csv
  hash: auto

model:
  type: xgboost
  hyperparameters:
    max_depth: 6
    learning_rate: 0.1
    n_estimators: 300

metrics:
  - name: auc
    threshold: 0.85
  - name: accuracy
    threshold: 0.80

governance:
  owner: [e-mail]
  tags:
    - compliance: gdpr
    - environment: production

deploy:
  realtime: true
  batch_output: ./outputs/predictions.csv

Run it:

godml run -f godml.yml

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Notebook API

For interactive work in Jupyter:

from godml import GodmlNotebook

nb = GodmlNotebook()
nb.load_data("./data/churn.csv", target="churn")
nb.train_model("xgboost", {"max_depth": 6, "n_estimators": 300})
nb.evaluate(["auc", "accuracy", "f1"])
nb.save_model("churn_v1")

AI-powered advisor

from godml.notebook_api import advisor_full_report, tune_model

# Get model + metric recommendations for your dataset
report = advisor_full_report(df, target="churn")
print(report["recommended_models"])   # ['xgboost', 'random_forest']
print(report["data_quality"])         # quality score + issues

# Auto-tune with Optuna
result = tune_model(
    model_type="xgboost",
    X=X_train, y=y_train,
    max_trials=50,
    metric="auc",
)
print(f"Best AUC: {result['best_score']:.4f}")

Supported model types

Key	Algorithm
xgboost / xgb	XGBoost
random_forest / rf	scikit-learn RandomForest
logistic_regression / logreg	scikit-learn LogisticRegression
lstm	LSTM forecasting (requires [deep])

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Compliance

from godml.compliance_service import PciDssCompliance, GdprCompliance

compliance = PciDssCompliance()
clean_df = compliance.apply(df)          # masks PAN, CVV, account numbers

gdpr = GdprCompliance()
report = gdpr.apply(df)                  # anonymizes PII per GDPR rules

Built-in compliance modules: PCI-DSS, GDPR, HIPAA, SOX.
Custom rules: subclass BaseCompliance and implement apply(df).

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Architecture

┌──────────────────────────────────────────────────────┐
│                    GODML Framework                   │
├────────────────┬─────────────┬───────────────────────┤
│  Interfaces    │  Notebook   │  CLI  │  REST API      │
├────────────────┴─────────────┴───────────────────────┤
│  Core Services                                       │
│  ┌───────────┐ ┌───────────┐ ┌──────────────────────┐│
│  │ Advisor   │ │ Config    │ │ Pipeline Engine      ││
│  └───────────┘ └───────────┘ └──────────────────────┘│
├──────────────────────────────────────────────────────┤
│  ML Services                                         │
│  ┌───────────┐ ┌───────────┐ ┌──────────────────────┐│
│  │ DataPrep  │ │ Model     │ │ Monitoring           ││
│  │ +PII scan │ │ Registry  │ │ +Drift detection     ││
│  └───────────┘ └───────────┘ └──────────────────────┘│
├──────────────────────────────────────────────────────┤
│  Providers:  MLflow │ SageMaker │ Docker │ Local      │
└──────────────────────────────────────────────────────┘

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Supply chain & security

GODML ships with a SLSA Level 3 supply chain — every release is built in an isolated GitHub Actions environment with unforgeable provenance.

Artifact	Standard	Signature	Transparency
sbom.spdx.json	SPDX 2.3	Cosign OIDC (keyless)	Rekor log
sbom.cyclonedx.json	CycloneDX 1.6	SLSA provenance	GitHub Release assets
provenance.intoto.jsonl	SLSA v1 / in-toto	slsa-github-generator	Rekor log

Verify the SBOM yourself

# Download from GitHub Releases
cosign verify-blob \
  --bundle sbom.spdx.bundle \
  --certificate-identity-regexp "https://github.com/DAGMALIA/godml/.github/workflows/safety_scan.yml" \
  --certificate-oidc-issuer "https://token.actions.githubusercontent.com" \
  sbom.spdx.json

Verify SLSA provenance

slsa-verifier verify-artifact dist/godml-*.whl \
  --provenance-path provenance.intoto.jsonl \
  --source-uri github.com/DAGMALIA/godml \
  --source-tag v1.1.0

CI security controls

Control	Tool	Status
SAST	Bandit	✅ Blocks on HIGH/CRITICAL
Dependency CVEs	pip-audit + Safety	✅ Weekly + per PR
SHA-pinned actions	Dependabot	✅ Auto-pinned
PyPI publish	OIDC Trusted Publisher	✅ No API tokens
Branch protection	GitHub Ruleset	✅ PR + status checks
Tag protection	GitHub Ruleset	✅ v* immutable
Score	OpenSSF Scorecard	✅ Published weekly

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CLI reference

godml init <project>         # scaffold new project
godml run -f godml.yml       # execute pipeline from config
godml deploy <project> <env> # deploy model to environment
godml --version              # print version

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Roadmap

v1.2.0 — Q3 2026

• Interactive drift dashboard (Streamlit)
• A/B testing framework
• Optuna distributed tuning

v1.3.0 — Q4 2026

• Kubernetes operator
• Real-time streaming inference
• Multi-tenant model registry

v2.0.0 — 2027

• Multi-cloud provider abstraction (Vertex AI, Azure ML)
• Federated learning support
• SOC2 / ISO27001 documentation kit

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Contributing

git clone https://github.com/DAGMALIA/godml.git
cd godml
pip install -e ".[dev]"
pytest tests/ --cov=godml

See CONTRIBUTING.md for branch conventions and PR checklist.

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License

MIT — see LICENSE.

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