Data Science & Statistical Modeling Reference Architecture - stanlypoc/AIRA GitHub Wiki

  1. Definition

A framework for implementing statistical modeling and data science workflows across classical and modern techniques, supporting the full lifecycle from experimentation to production deployment. Covers six core capability domains with vendor-specific implementations.

  1. Architecture Diagrams

2.1 Classical Statistical Models

2.1.1 Logical Architecture

graph LR
    A[Structured Data] --> B[Feature Engineering]
    B --> C[Model Training]
    C --> D[Validation]
    D --> E[Deployment]

2.1.2 Azure

graph LR
    A[Azure SQL] --> B[Synapse ML]
    B --> C[Linear Regression]
    C --> D[MLflow Tracking]

2.1.3 AWS

graph LR
    A[Redshift] --> B[SageMaker]
    B --> C[GLM]
    C --> D[SageMaker Experiments]

2.1.4 GCP

graph LR
    A[BigQuery] --> B[Vertex AI]
    B --> C[Statsmodels]
    C --> D[Vertex ML Metadata]

2.1.5 Open Source

graph LR
    A[PostgreSQL] --> B[Scikit-learn]
    B --> C[Statsmodels]
    C --> D[MLflow]

2.2 Clustering & Dimensionality Reduction 2.2.1 Logical Architecture

graph LR
    A[High-Dim Data] --> B[Preprocessing]
    B --> C[Algorithm Selection]
    C --> D[Visualization]

2.2.2 Azure

graph LR
    A[ADLS] --> B[Databricks]
    B --> C[K-Means/PCA]
    C --> D[Power BI]

2.2.3 AWS

graph LR
    A[S3] --> B[EMR]
    B --> C[UMAP/t-SNE]
    C --> D[QuickSight]

2.2.4 GCP

graph LR
    A[BigQuery ML] --> B[Vertex AI]
    B --> C[Autoencoder]
    C --> D[Looker]

2.2.5 Open Source

graph LR
    A[Parquet] --> B[Dask]
    B --> C[UMAP]
    C --> D[Plotly]

2.3 Time Series Analysis 2.3.1 Logical Architecture

graph LR
    A[Time-Stamped Data] --> B[Feature Extraction]
    B --> C[Forecasting]
    C --> D[Monitoring]

2.3.2 Azure

graph LR
    A[Event Hub] --> B[Synapse]
    B --> C[Prophet/ARIMA]
    C --> D[Azure Monitor]

2.3.3 AWS

graph LR
    A[Kinesis] --> B[Redshift ML]
    B --> C[DeepAR]
    C --> D[CloudWatch]

2.3.4 GCP

graph LR
    A[PubSub] --> B[BigQuery ML]
    B --> C[TensorFlow Probability]
    C --> D[Cloud Monitoring]

2.3.5 Open Source

graph LR
    A[Kafka] --> B[Spark]
    B --> C[statsforecast]
    C --> D[Prometheus]

2.4 Optimization & Simulation 2.4.1 Logical Architecture

graph LR
    A[Constraints] --> B[Model Formulation]
    B --> C[Solver]
    C --> D[Scenario Analysis]

2.4.2 Azure

graph LR
    A[Azure ML] --> B[OR-Tools]
    B --> C[SimPy]
    C --> D[Power BI]

2.4.3 AWS

graph LR
    A[SageMaker] --> B[Gurobi]
    B --> C[AnyLogic]
    C --> D[QuickSight]

2.4.4 GCP

graph LR
    A[Vertex AI] --> B[OR-Tools]
    B --> C[SimJulia]
    C --> D[Looker]

2.4.5 Open Source

graph LR
    A[Pyomo] --> B[SciPy]
    B --> C[SimPy]
    C --> D[Streamlit]

2.5 Specialized Analysis 2.5.1 Logical Architecture

graph LR
    A[Domain Data] --> B[Custom Pipelines]
    B --> C[Specialized Libraries]
    C --> D[Visualization]

2.5.2 Azure

graph LR
    A[Purview] --> B[Synapse]
    B --> C[Survival Analysis]
    C --> D[Power BI]

2.5.3 AWS

graph LR
    A[DataZone] --> B[SageMaker]
    B --> C[Bayesian Networks]
    C --> D[QuickSight]

2.5.4 GCP

graph LR
    A[Dataplex] --> B[Vertex AI]
    B --> C[Causal Inference]
    C --> D[Looker]

2.5.5 Open Source

graph LR
    A[Pandas] --> B[PyMC]
    B --> C[Lifelines]
    C --> D[Plotly]

2.6 Recommender Systems 2.6.1 Logical Architecture

graph LR
    A[User-Item Data] --> B[Matrix Factorization]
    B --> C[Ranking]
    C --> D[AB Testing]

2.6.2 Azure

graph LR
    A[Cosmos DB] --> B[Personalizer]
    B --> C[Azure ML]
    C --> D[App Insights]

2.6.3 AWS

graph LR
    A[DynamoDB] --> B[Personalize]
    B --> C[SageMaker]
    C --> D[CloudWatch]

2.6.4 GCP

graph LR
    A[Firestore] --> B[Recommendations AI]
    B --> C[Vertex AI]
    C --> D[Analytics Hub]

2.6.5 Open Source

graph LR
    A[Redis] --> B[LightFM]
    B --> C[XGBoost]
    C --> D[MLflow]
  1. Cross-Cutting Concerns

3.1 Security Logical Architecture

graph LR
    A[Data] --> B[Access Control]
    B --> C[Encryption]
    C --> D[Audit]

Azure

graph LR
    A[Purview] --> B[RBAC]
    B --> C[Key Vault]
    C --> D[Log Analytics]

AWS

graph LR
    A[Macie] --> B[IAM]
    B --> C[KMS]
    C --> D[CloudTrail]

GCP

graph LR
    A[Data Catalog] --> B[IAM]
    B --> C[Cloud KMS]
    C --> D[Audit Logs]

Open Source

graph LR
    A["Apache Ranger"] --> B[OPA]
    B --> C[Vault]
    C --> D["Fluentd"]

3.2 Observability Logical Architecture

graph LR
    A[Metrics] --> B[Logs]
    B --> C[Traces]
    C --> D[Alerts]

Azure

graph LR
    A[Monitor] --> B[Log Analytics]
    B --> C[App Insights]
    C --> D[Action Groups]

AWS

graph LR
    A[CloudWatch] --> B[OpenSearch]
    B --> C[X-Ray]
    C --> D[SNS]

GCP

graph LR
    A[Cloud Monitoring] --> B[Logging]
    B --> C[Trace]
    C --> D[Alerting]

Open Source

graph LR
    A[Prometheus] --> B[Loki]
    B --> C[Jaeger]
    C --> D[Alertmanager]

3.3 CI/CD Logical Architecture

graph LR
    A[Code] --> B[Build]
    B --> C[Test]
    C --> D[Deploy]

Azure

graph LR
    A[DevOps] --> B[ML Pipelines]
    B --> C[Test Frameworks]
    C --> D[AKS]

AWS

graph LR
    A[CodePipeline] --> B[SageMaker Pipelines]
    B --> C[PyTest]
    C --> D[EKS]

GCP

graph LR
    A[Cloud Build] --> B[Vertex Pipelines]
    B --> C[PyTest]
    C --> D[GKE]

Open Source

graph LR
    A[GitHub Actions] --> B[MLflow Projects]
    B --> C[PyTest]
    C --> D[Kubeflow]
  1. Integration Patterns

4.1 Cloud-Native

graph LR
    A[Data Lake] --> B[Feature Store]
    B --> C[Model Training]
    C --> D[Serving]

4.2 Hybrid/Multi-Cloud

graph LR
    A[On-Prem] --> B[Cloud Gateway]
    B --> C[Unified Catalog]
    C --> D[Cross-Cloud Query]

4.3 On-Premises

graph LR
    A[Edge Devices] --> B[Local Processing]
    B --> C[Sync Service]
    C --> D[Central Warehouse]
  1. Architectural Principles
  2. Reproducibility: Version data, code, and models
  3. Scalability: Distributed processing where needed
  4. Interpretability: Explainable models with tracking
  5. Modularity: Swappable components
  6. Automation: CI/CD for model lifecycle
  7. Governance: Compliant and auditable
  8. Cost Awareness: Right-size resources
  1. Implementation Guide:
  2. Start with logical architecture
  3. Select vendor stack
  4. Apply cross-cutting concerns
  5. Implement integration patterns
  6. Validate against principles