1. Traditional Machine Learning Reference Architecture - stanlypoc/AIRA GitHub Wiki

Traditional Machine Learning Reference Architecture

1. Introduction

1.1 Purpose

Standardized architectural patterns for implementing traditional ML systems (non-deep learning) across maturity levels.

1.2 Audience

  • Data Engineers
  • ML Engineers
  • Solution Architects
  • Security/Compliance Teams

1.3 Scope & Applicability

In Scope:

  • Supervised/unsupervised learning
  • Batch and real-time inference
  • On-prem and cloud deployments

Out of Scope:

  • Deep learning architectures
  • Quantum ML
  • Edge deployment patterns

1.4 Assumptions & Constraints

Prerequisites:

  • Basic data pipeline exists
  • Labeled training data available

Technical Constraints:

  • CPU-bound training
  • <1TB dataset sizes

Ethical Boundaries:

  • No PII in model features
  • Human-in-the-loop for critical decisions

1.6 Example Models

  • Logistic regression
  • Random forests
  • SVM classifiers
  • XGBoost models

2. Architectural Principles

Standards Compliance

Security & Privacy

mermaid
graph LR
    A[Data] --> B[Encryption at Rest]
    B --> C[Role-Based Access]
    C --> D[Audit Logging]

Ethical AI

mermaid
graph LR
    E[Training Data] --> F[Bias Detection]
    F --> G[Fairness Metrics]
    G --> H[Human Review]

Operational Mandates

5 Golden Rules:

  1. Never train on production data copies
  2. Always version model artifacts
  3. Validate features pre-inference
  4. Monitor concept drift
  5. Document all decisions

3. Architecture by Technology Level

3.1 Level 1: Basic (Rule-Based & Manual)

Definition: Manual feature engineering with scheduled batch training

Logical Architecture:

mermaid
graph LR
    DS[Data Source] --> FE[Feature Engineering]
    FE --> TR[Training]
    TR --> MO[Model Object]
    MO --> BI[Batch Inference]

Azure Implementation:

  • Data: Azure SQL DB
  • Compute: Azure VMs
  • Orchestration: Azure Data Factory

Cross-Cutting Concerns:

Area Implementation
Security Network isolation
Performance Vertical scaling
Observability Custom logging

3.2 Level 2: Assisted (Automated & Scalable)

Definition: Automated pipelines with CI/CD

Logical Architecture:

mermaid
graph LR
    DS[Data Lake] --> AP[Auto Feature Pipeline]
    AP --> AT[AutoML]
    AT --> MR[Model Registry]
    MR --> RI[Real-time Inference]

**AWS Implementation:

  • Data: S3 + Glue
  • Compute: SageMaker
  • Serving: Lambda + API Gateway

3.3 Level 3: Augmented (AI-Enhanced)

Definition: Self-tuning systems with explainability

Logical Architecture:

mermaid
graph LR
    ST[Streaming Data] --> AP[Adaptive Preprocessor]
    AP --> MT[Meta-Learner]
    MT --> EX[Explainability Service]
    EX --> DI[Drift-Aware Inference]

4.0 Glossary & References

Terminology:

  • Feature Store: Centralized feature repository
  • Concept Drift: Model accuracy decay over time

Related Documents: