# 🤖 GODML — Governed, Observable & Declarative Machine Learning
**Framework de MLOps con Gobernanza, Trazabilidad y Supply Chain Verificada**
[](https://pypi.org/project/godml/)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://search.sigstore.dev/?q=DAGMALIA)
[](https://slsa.dev/)
---
## 🚀 GODML v1.0.1 — *Stable Governance Release*
La versión 1.0.1 marca un **hito en la madurez del framework**, incorporando trazabilidad completa, publicación verificada en PyPI y una cadena de suministro auditada mediante **Sigstore + SLSA**.
### 🧩 Características clave
- ✅ Framework **estable y modular**
- 🔐 Supply Chain firmada (SBOM + Provenance)
- 🧾 Cumplimiento **SLSA v1 y SPDX**
- 📦 Publicación segura via **PyPI Trusted Publisher (OIDC)**
- 🧠 Notebook API integrada (`GodmlNotebook`)
- ⚙️ CLI declarativa (`godml run -f godml.yml`)
- 🪶 Licencia MIT
---
## 🔐 Supply Chain & Seguridad
GODML adopta un enfoque de **transparencia verificable**, integrando herramientas de seguridad nativas:
| Artefacto | Estándar | Firma | Transparencia |
|------------|-----------|--------|----------------|
| `sbom.spdx.json` | SPDX | ✅ Cosign OIDC | [Rekor Log](https://search.sigstore.dev/) |
| `provenance.json` | SLSA v1 | ✅ Cosign OIDC | [Rekor Log](https://search.sigstore.dev/) |
### 📜 Verificación reproducible
```bash
cosign verify-blob \
--bundle sbom.spdx.bundle \
--certificate-identity-regexp "github.com/DAGMALIA" \
--certificate-oidc-issuer "https://token.actions.githubusercontent.com" \
sbom.spdx.json
```
> Proyecto de Machine Learning generado automáticamente con **GODML Framework** - Governed, Observable & Declarative ML
---
🚀 GODML Framework
https://pypi.org/project/godml/
https://python.org
LICENSE
https://pypi.org/project/godml/
Governed, Observable & Declarative Machine Learning
Enterprise-grade MLOps platform for production-ready ML pipelines
🚀 Quick Start • 📖 Documentation • 🏗️ Architecture • 🤝 Contributing
------------------------------------------------------------------------------------------------------------------------
```text
🎯 Overview
GODML is a comprehensive MLOps framework that unifies Governance, Observability, and Declarative configuration for enterprise Machine Learning workflows. Built for organizations that require complete traceability, regulatory compliance, and scalable model deployment.
```
```text
🌟 Key Features
* 🏛️ Governance: Automatic traceability, metadata management, and compliance
* 👁️ Observability: Complete MLflow integration with real-time monitoring
* 📄 Declarative: Simple YAML configuration for reproducible pipelines
* 🚀 Production-Ready: Docker, Kubernetes, and cloud-native deployment
* 🛡️ Compliance: Built-in PCI-DSS, GDPR, and HIPAA support
* 🧠 AI-Powered: LLM-assisted pipeline optimization and recommendations
```
```text
GODML Performance Metrics
🎯 Business Impact
Metric Traditional ML With GODML Improvement
Time to Production 6 months 2 weeks 92% faster
Model Accuracy 78% 89% 14% better
Compliance Violations 12/year 0/year 100% reduction
Operational Cost $50K/month $15K/month 70% savings
```
🚀 Quick Start
Installation
```bash
# Install GODML
pip install godml
# Verify installation
godml --version
```
Create Your First Project
```bash
# Initialize new project
godml init my-ml-project
cd my-ml-project
# Configure your pipeline
vim godml.yml
# Train your model
godml run -f godml.yml
# Deploy to production
godml deploy my-ml-project production
```
📄 Basic Configuration
```yaml title="godml.yml (mínimo viable)"
name: customer-churn-prediction
version: 1.0.0
provider: mlflow
dataset:
uri: ./data/customer_data.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: "ml-team@company.com"
tags:
- project: customer-retention
- compliance: gdpr
- environment: production
deploy:
realtime: true
batch_output: ./outputs/predictions.csv
```
🧪 Notebook Integration
```text
Quick Training
```
```python
from godml import GodmlNotebook, quick_train
# Method 1: Full pipeline setup
godml = GodmlNotebook()
godml.create_pipeline(
name="churn-model",
model_type="xgboost",
hyperparameters={"max_depth": 6, "eta": 0.1},
dataset_path="./data/churn.csv"
)
godml.train()
godml.save_model(model_name="churn_v1", environment="production")
# Method 2: One-liner training
quick_train(
model_type="random_forest",
hyperparameters={"n_estimators": 300},
dataset_path="./data/churn.csv"
)
```
AI-Powered Optimization
```python
from godml.notebook_api import advisor_full_report, tune_model
# Get AI recommendations
report = advisor_full_report(df, target="churn")
print(f"Recommended models: {report['models']}")
print(f"Suggested metrics: {report['metrics']}")
# Auto-tune hyperparameters
result = tune_model(
model_type="xgboost",
X=X_train, y=y_train,
max_trials=100,
metric="roc_auc"
)
print(f"Best AUC: {result['best_score']:.4f}")
```
🔧 System Architecture
```text
┌─────────────────────────────────────────────────────────────────┐
│ 🎯 GODML Framework │
├─────────────────────────────────────────────────────────────────┤
│ Frontend Layer │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────────┐ │
│ │ 🌐 Web UI │ │ 📓 Jupyter │ │ 🖥️ CLI Tool │ │
│ └─────────────┘ └─────────────┘ └─────────────────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ API Gateway │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ 🚪 FastAPI Gateway (Authentication & Routing) │ │
│ └─────────────────────────────────────────────────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ Core Services │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────────┐ │
│ │🧠 Advisor │ │⚙️ Config │ │🎯 Pipeline Engine │ │
│ │Service │ │Service │ │ │ │
│ └─────────────┘ └─────────────┘ └─────────────────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ ML Services │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────────┐ │
│ │🔄 DataPrep │ │🤖 Model │ │📊 Monitoring │ │
│ │Service │ │Service │ │Service │ │
│ └─────────────┘ └─────────────┘ └─────────────────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ Infrastructure │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────────┐ │
│ │💾 PostgreSQL│ │🗄️ Redis │ │☁️ Cloud Storage │ │
│ │Database │ │Cache │ │(S3/Azure/GCS) │ │
│ └─────────────┘ └─────────────┘ └─────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
```
🌊 Data Flow Pipeline
```text
📊 Raw Data → 🔄 DataPrep → 🛡️ Compliance → 🤖 Training → 📈 Validation → 📦 Registry → 🚀 Deployment → 📊 Monitoring
↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓
S3/Local Transforms PII Detection XGBoost/RF Cross-Val MLflow Store Docker/K8s Drift Detection
🛡️ Enterprise Features
Compliance & Security
🔒 Data Protection: Encryption at rest and in transit
🛡️ PII Detection: Automatic identification and masking
📋 Regulatory Support: GDPR, PCI-DSS, HIPAA, SOX compliance
🔍 Audit Trail: Complete lineage and change tracking
Scalability & Performance
☸️ Kubernetes Native: Cloud-native deployment
🔄 Auto-scaling: Dynamic resource allocation
⚡ Low Latency: <50ms prediction SLA
📈 High Throughput: 10K+ predictions/second
🏢 Enterprise Use Cases
Financial Services
* Fraud Detection: Real-time transaction scoring with PCI-DSS compliance
* Credit Risk: Automated underwriting with regulatory reporting
* Algorithmic Trading: Low-latency prediction models
Healthcare
* Diagnostic Assistance: HIPAA-compliant medical image analysis
* Drug Discovery: Molecular property prediction pipelines
* Clinical Trials: Patient stratification and outcome prediction
Retail & E-commerce
* Recommendation Systems: Personalized product suggestions
* Demand Forecasting: Inventory optimization models
* Price Optimization: Dynamic pricing strategies
```
🛠️ CLI Reference
Project Management
```bash
godml init <project-name> # Initialize new project
godml run -f <config.yml> # Execute pipeline
godml validate -f <config.yml> # Validate configuration
```
Model Management
```bash
godml model list --env=production # List models by environment
godml model promote <name> --to=prod # Promote model between environments
godml model rollback <name> --version=1.2 # Rollback to previous version
```
Deployment
```bash
godml deploy <project> <env> # Deploy to environment
godml scale <deployment> --replicas=5 # Scale deployment
godml logs <service> --follow # View service logs
```
🌐 Cloud Deployment
Docker Deployment
```bash
# Build and run
docker build -t my-godml-model .
docker run -p 8080:8080 my-godml-model
# Health check
curl http://localhost:8080/health
```
```Text
📈 Roadmap
🎯 2025 Q2 - Intelligence
* 🧠 Advanced AutoML capabilities
* 🤖 GPT-4 powered pipeline generation
* 📊 Interactive web dashboard
* 🔍 Explainable AI integration
🎯 2025 Q3 - Scale
* ☸️ Kubernetes operator
* 🌊 Real-time streaming ML
* 🔄 A/B testing framework
* 📈 Advanced drift detection
🎯 2025 Q4 - Enterprise
* 🏢 Multi-tenant architecture
* 🔒 Zero-trust security model
* 🌐 Global edge deployment
* 📋 SOC2/ISO27001 certification
🤝 Contributing
We welcome contributions! Please see our Contributing Guide for details.
Development Setup
-- Next Repo
📄 License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
📞 Support
```
* Enterprise Support: mailto:agtzrubio@dagmalia.com
* Community Support: mailto:agtzrubio@dagmalia.com
* Documentation: https://godmlcore.com/
* Status Page: https://godmlcore.com/
Built with ❤️ by the GODM
https://github.com/godml/godml (Proximamente)
https://twitter.com/godml_ai (Proximamente)
https://linkedin.com/company/godml (Proximamente)
Transforming Enterprise ML Operations 🚀
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"description": "# \ud83e\udd16 GODML \u2014 Governed, Observable & Declarative Machine Learning \r\n**Framework de MLOps con Gobernanza, Trazabilidad y Supply Chain Verificada**\r\n\r\n[](https://pypi.org/project/godml/)\r\n[](https://www.python.org/downloads/)\r\n[](https://opensource.org/licenses/MIT)\r\n[](https://search.sigstore.dev/?q=DAGMALIA)\r\n[](https://slsa.dev/)\r\n\r\n---\r\n\r\n## \ud83d\ude80 GODML v1.0.1 \u2014 *Stable Governance Release*\r\n\r\nLa versi\u00f3n 1.0.1 marca un **hito en la madurez del framework**, incorporando trazabilidad completa, publicaci\u00f3n verificada en PyPI y una cadena de suministro auditada mediante **Sigstore + SLSA**.\r\n\r\n### \ud83e\udde9 Caracter\u00edsticas clave\r\n- \u2705 Framework **estable y modular**\r\n- \ud83d\udd10 Supply Chain firmada (SBOM + Provenance)\r\n- \ud83e\uddfe Cumplimiento **SLSA v1 y SPDX**\r\n- \ud83d\udce6 Publicaci\u00f3n segura via **PyPI Trusted Publisher (OIDC)**\r\n- \ud83e\udde0 Notebook API integrada (`GodmlNotebook`)\r\n- \u2699\ufe0f CLI declarativa (`godml run -f godml.yml`)\r\n- \ud83e\udeb6 Licencia MIT\r\n\r\n---\r\n\r\n## \ud83d\udd10 Supply Chain & Seguridad\r\n\r\nGODML adopta un enfoque de **transparencia verificable**, integrando herramientas de seguridad nativas:\r\n\r\n| Artefacto | Est\u00e1ndar | Firma | Transparencia |\r\n|------------|-----------|--------|----------------|\r\n| `sbom.spdx.json` | SPDX | \u2705 Cosign OIDC | [Rekor Log](https://search.sigstore.dev/) |\r\n| `provenance.json` | SLSA v1 | \u2705 Cosign OIDC | [Rekor Log](https://search.sigstore.dev/) |\r\n\r\n### \ud83d\udcdc Verificaci\u00f3n reproducible\r\n\r\n```bash\r\ncosign verify-blob \\\r\n --bundle sbom.spdx.bundle \\\r\n --certificate-identity-regexp \"github.com/DAGMALIA\" \\\r\n --certificate-oidc-issuer \"https://token.actions.githubusercontent.com\" \\\r\n sbom.spdx.json\r\n```\r\n\r\n> Proyecto de Machine Learning generado autom\u00e1ticamente con **GODML Framework** - Governed, Observable & Declarative ML\r\n\r\n---\r\n\r\n \ud83d\ude80 GODML Framework\r\n https://pypi.org/project/godml/\r\n https://python.org\r\n LICENSE\r\n https://pypi.org/project/godml/\r\n Governed, Observable & Declarative Machine Learning\r\n Enterprise-grade MLOps platform for production-ready ML pipelines\r\n \ud83d\ude80 Quick Start \u2022 \ud83d\udcd6 Documentation \u2022 \ud83c\udfd7\ufe0f Architecture \u2022 \ud83e\udd1d Contributing\r\n------------------------------------------------------------------------------------------------------------------------\r\n```text\r\n\ud83c\udfaf Overview\r\n\r\nGODML is a comprehensive MLOps framework that unifies Governance, Observability, and Declarative configuration for enterprise Machine Learning workflows. Built for organizations that require complete traceability, regulatory compliance, and scalable model deployment.\r\n```\r\n\r\n```text\r\n\ud83c\udf1f Key Features\r\n\r\n * \ud83c\udfdb\ufe0f Governance: Automatic traceability, metadata management, and compliance\r\n\r\n * \ud83d\udc41\ufe0f Observability: Complete MLflow integration with real-time monitoring\r\n\r\n * \ud83d\udcc4 Declarative: Simple YAML configuration for reproducible pipelines\r\n\r\n * \ud83d\ude80 Production-Ready: Docker, Kubernetes, and cloud-native deployment\r\n\r\n * \ud83d\udee1\ufe0f Compliance: Built-in PCI-DSS, GDPR, and HIPAA support\r\n\r\n * \ud83e\udde0 AI-Powered: LLM-assisted pipeline optimization and recommendations\r\n```\r\n\r\n```text\r\nGODML Performance Metrics\r\n\r\n\ud83c\udfaf Business Impact\r\n\r\n Metric\t Traditional ML\t With GODML\t Improvement\r\nTime to Production\t 6 months\t 2 weeks\t 92% faster\r\nModel Accuracy\t 78%\t 89%\t 14% better\r\nCompliance Violations\t 12/year\t 0/year\t 100% reduction\r\nOperational Cost\t $50K/month\t $15K/month\t 70% savings\r\n```\r\n\r\n\ud83d\ude80 Quick Start\r\n\r\nInstallation\r\n\r\n```bash\r\n# Install GODML\r\npip install godml\r\n\r\n# Verify installation\r\ngodml --version\r\n```\r\nCreate Your First Project\r\n\r\n```bash\r\n# Initialize new project\r\ngodml init my-ml-project\r\ncd my-ml-project\r\n\r\n# Configure your pipeline\r\nvim godml.yml\r\n\r\n# Train your model\r\ngodml run -f godml.yml\r\n\r\n# Deploy to production\r\ngodml deploy my-ml-project production\r\n```\r\n\r\n\ud83d\udcc4 Basic Configuration\r\n\r\n```yaml title=\"godml.yml (m\u00ednimo viable)\"\r\nname: customer-churn-prediction\r\nversion: 1.0.0\r\nprovider: mlflow\r\n\r\ndataset:\r\n uri: ./data/customer_data.csv\r\n hash: auto\r\n\r\nmodel:\r\n type: xgboost\r\n hyperparameters:\r\n {\"max_depth\": 6}\r\n {\"learning_rate\": 0.1}\r\n {\"n_estimators\": 300}\r\n\r\nmetrics:\r\n- name: auc\r\n threshold: 0.85\r\n- name: accuracy\r\n threshold: 0.80\r\n\r\ngovernance:\r\n owner: \"ml-team@company.com\"\r\n tags:\r\n - project: customer-retention\r\n - compliance: gdpr\r\n - environment: production\r\n\r\ndeploy:\r\n realtime: true\r\n batch_output: ./outputs/predictions.csv\r\n```\r\n\ud83e\uddea Notebook Integration\r\n\r\n```text\r\nQuick Training\r\n```\r\n\r\n```python\r\nfrom godml import GodmlNotebook, quick_train\r\n\r\n# Method 1: Full pipeline setup\r\ngodml = GodmlNotebook()\r\ngodml.create_pipeline(\r\n name=\"churn-model\",\r\n model_type=\"xgboost\",\r\n hyperparameters={\"max_depth\": 6, \"eta\": 0.1},\r\n dataset_path=\"./data/churn.csv\"\r\n)\r\ngodml.train()\r\ngodml.save_model(model_name=\"churn_v1\", environment=\"production\")\r\n\r\n# Method 2: One-liner training\r\nquick_train(\r\n model_type=\"random_forest\",\r\n hyperparameters={\"n_estimators\": 300},\r\n dataset_path=\"./data/churn.csv\"\r\n)\r\n```\r\nAI-Powered Optimization\r\n\r\n```python\r\nfrom godml.notebook_api import advisor_full_report, tune_model\r\n\r\n# Get AI recommendations\r\nreport = advisor_full_report(df, target=\"churn\")\r\nprint(f\"Recommended models: {report['models']}\")\r\nprint(f\"Suggested metrics: {report['metrics']}\")\r\n\r\n# Auto-tune hyperparameters\r\nresult = tune_model(\r\n model_type=\"xgboost\",\r\n X=X_train, y=y_train,\r\n max_trials=100,\r\n metric=\"roc_auc\"\r\n)\r\nprint(f\"Best AUC: {result['best_score']:.4f}\")\r\n```\r\n\r\n\ud83d\udd27 System Architecture\r\n```text\r\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n\u2502 \ud83c\udfaf GODML Framework \u2502\r\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\r\n\u2502 Frontend Layer \u2502\r\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\r\n\u2502 \u2502 \ud83c\udf10 Web UI \u2502 \u2502 \ud83d\udcd3 Jupyter \u2502 \u2502 \ud83d\udda5\ufe0f CLI Tool \u2502 \u2502\r\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\r\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\r\n\u2502 API Gateway \u2502\r\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\r\n\u2502 \u2502 \ud83d\udeaa FastAPI Gateway (Authentication & Routing) \u2502 \u2502\r\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 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\u2502 \u2502 \u2502 \u2502\r\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\r\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\r\n\u2502 ML Services \u2502\r\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\r\n\u2502 \u2502\ud83d\udd04 DataPrep \u2502 \u2502\ud83e\udd16 Model \u2502 \u2502\ud83d\udcca Monitoring \u2502 \u2502\r\n\u2502 \u2502Service \u2502 \u2502Service \u2502 \u2502Service \u2502 \u2502\r\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\r\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\r\n\u2502 Infrastructure \u2502\r\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\r\n\u2502 \u2502\ud83d\udcbe PostgreSQL\u2502 \u2502\ud83d\uddc4\ufe0f Redis \u2502 \u2502\u2601\ufe0f Cloud Storage \u2502 \u2502\r\n\u2502 \u2502Database \u2502 \u2502Cache \u2502 \u2502(S3/Azure/GCS) \u2502 \u2502\r\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\r\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n```\r\n\r\n\ud83c\udf0a Data Flow Pipeline\r\n\r\n```text\r\n\ud83d\udcca Raw Data \u2192 \ud83d\udd04 DataPrep \u2192 \ud83d\udee1\ufe0f Compliance \u2192 \ud83e\udd16 Training \u2192 \ud83d\udcc8 Validation \u2192 \ud83d\udce6 Registry \u2192 \ud83d\ude80 Deployment \u2192 \ud83d\udcca Monitoring\r\n \u2193 \u2193 \u2193 \u2193 \u2193 \u2193 \u2193 \u2193\r\n S3/Local Transforms PII Detection XGBoost/RF Cross-Val MLflow Store Docker/K8s Drift Detection\r\n\r\n\r\n\ud83d\udee1\ufe0f Enterprise Features\r\n\r\nCompliance & Security\r\n\r\n\ud83d\udd12 Data Protection: Encryption at rest and in transit\r\n\r\n\ud83d\udee1\ufe0f PII Detection: Automatic identification and masking\r\n\r\n\ud83d\udccb Regulatory Support: GDPR, PCI-DSS, HIPAA, SOX compliance\r\n\r\n\ud83d\udd0d Audit Trail: Complete lineage and change tracking\r\n\r\nScalability & Performance\r\n\r\n\u2638\ufe0f Kubernetes Native: Cloud-native deployment\r\n\r\n\ud83d\udd04 Auto-scaling: Dynamic resource allocation\r\n\r\n\u26a1 Low Latency: <50ms prediction SLA\r\n\r\n\ud83d\udcc8 High Throughput: 10K+ predictions/second\r\n\r\n\r\n\ud83c\udfe2 Enterprise Use Cases\r\n\r\nFinancial Services\r\n\r\n * Fraud Detection: Real-time transaction scoring with PCI-DSS compliance\r\n\r\n * Credit Risk: Automated underwriting with regulatory reporting\r\n\r\n * Algorithmic Trading: Low-latency prediction models\r\n\r\nHealthcare\r\n\r\n * Diagnostic Assistance: HIPAA-compliant medical image analysis\r\n\r\n * Drug Discovery: Molecular property prediction pipelines\r\n\r\n * Clinical Trials: Patient stratification and outcome prediction\r\n\r\nRetail & E-commerce\r\n\r\n * Recommendation Systems: Personalized product suggestions\r\n\r\n * Demand Forecasting: Inventory optimization models\r\n\r\n * Price Optimization: Dynamic pricing strategies\r\n\r\n```\r\n\r\n\ud83d\udee0\ufe0f CLI Reference\r\n\r\nProject Management\r\n\r\n```bash\r\ngodml init <project-name> # Initialize new project\r\ngodml run -f <config.yml> # Execute pipeline\r\ngodml validate -f <config.yml> # Validate configuration\r\n```\r\n\r\nModel Management\r\n\r\n```bash\r\ngodml model list --env=production # List models by environment\r\ngodml model promote <name> --to=prod # Promote model between environments\r\ngodml model rollback <name> --version=1.2 # Rollback to previous version\r\n```\r\n\r\nDeployment\r\n\r\n```bash\r\ngodml deploy <project> <env> # Deploy to environment\r\ngodml scale <deployment> --replicas=5 # Scale deployment\r\ngodml logs <service> --follow # View service logs\r\n```\r\n\r\n\ud83c\udf10 Cloud Deployment\r\n\r\nDocker Deployment\r\n\r\n```bash\r\n# Build and run\r\ndocker build -t my-godml-model .\r\ndocker run -p 8080:8080 my-godml-model\r\n\r\n# Health check\r\ncurl http://localhost:8080/health\r\n```\r\n\r\n```Text\r\n\ud83d\udcc8 Roadmap\r\n\r\n\ud83c\udfaf 2025 Q2 - Intelligence\r\n * \ud83e\udde0 Advanced AutoML capabilities\r\n * \ud83e\udd16 GPT-4 powered pipeline generation\r\n * \ud83d\udcca Interactive web dashboard\r\n * \ud83d\udd0d Explainable AI integration\r\n\r\n\ud83c\udfaf 2025 Q3 - Scale\r\n * \u2638\ufe0f Kubernetes operator\r\n * \ud83c\udf0a Real-time streaming ML\r\n * \ud83d\udd04 A/B testing framework\r\n * \ud83d\udcc8 Advanced drift detection\r\n\r\n\ud83c\udfaf 2025 Q4 - Enterprise\r\n * \ud83c\udfe2 Multi-tenant architecture\r\n * \ud83d\udd12 Zero-trust security model\r\n * \ud83c\udf10 Global edge deployment\r\n * \ud83d\udccb SOC2/ISO27001 certification\r\n\r\n\ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! Please see our Contributing Guide for details.\r\n\r\nDevelopment Setup\r\n\r\n-- Next Repo\r\n\r\n\ud83d\udcc4 License\r\nThis project is licensed under the Apache License 2.0 - see the LICENSE file for details.\r\n\r\n\ud83d\udcde Support\r\n```\r\n\r\n * Enterprise Support: mailto:agtzrubio@dagmalia.com\r\n * Community Support: mailto:agtzrubio@dagmalia.com\r\n * Documentation: https://godmlcore.com/\r\n * Status Page: https://godmlcore.com/\r\n\r\n\r\n\r\n Built with \u2764\ufe0f by the GODM\r\n https://github.com/godml/godml (Proximamente)\r\n https://twitter.com/godml_ai (Proximamente)\r\n https://linkedin.com/company/godml (Proximamente)\r\n Transforming Enterprise ML Operations \ud83d\ude80\r\n",
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