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ai-ml-integration.md

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AI and Machine Learning Integration Standards

This document outlines the standards and best practices for incorporating AI and machine learning capabilities into Bayat projects.

Model Development and Selection

Technology Selection

  • Deep Learning Frameworks: TensorFlow, PyTorch, or JAX based on project requirements
  • Traditional ML Libraries: scikit-learn for classical ML algorithms
  • AutoML Platforms: For rapid prototyping and baseline model development
  • Third-party AI Services: Guidelines for evaluating and integrating Azure AI, Google Cloud AI, AWS AI services, OpenAI APIs, etc.

Model Types and Use Cases

  • Classification Models: Standards for problem formulation, training, and evaluation
  • Regression Models: Guidelines for numerical prediction tasks
  • Generative Models: Standards for text, image, and audio generation
  • Recommendation Systems: Guidelines for personalization features
  • NLP Models: Standards for text analysis, sentiment analysis, and language understanding
  • Computer Vision Models: Guidelines for image and video analysis

Development Lifecycle

Data Management

  1. Data Collection and Preparation:

    • Standards for data gathering and labeling
    • Guidelines for dataset splitting (train/validation/test)
    • Data augmentation best practices
    • Privacy and bias considerations in data collection

  2. Data Versioning:

    • Required metadata for datasets
    • Versioning standards using DVC or similar tools
    • Storage and access control requirements

Model Training

  1. Experiment Tracking:

    • Required use of tools like MLflow, Weights & Biases, or similar
    • Standardized metrics tracking
    • Hyperparameter logging requirements

  2. Training Infrastructure:

    • Resource allocation guidelines
    • GPU/TPU usage standards
    • Containerization requirements for training environments

Model Evaluation

  1. Evaluation Metrics:

    • Domain-specific metrics for different model types
    • Statistical significance testing requirements
    • Comparison against baseline models

  2. Model Testing:

    • Validation for data drift
    • Adversarial testing standards
    • Fairness and bias evaluation requirements

Model Deployment

  1. Serving Infrastructure:

    • Guidelines for model serving (TensorFlow Serving, TorchServe, etc.)
    • Scalability requirements
    • Latency and throughput standards

  2. Versioning and Rollback:

    • Model versioning practices
    • A/B testing requirements for new models
    • Rollback procedures

Integration Patterns

Architecture Patterns

  1. API-based Integration:

    • REST API standards for model serving
    • Request/response formats
    • Error handling patterns

  2. Batch Processing:

    • Standards for offline prediction jobs
    • Pipeline design patterns
    • Scheduling and monitoring requirements

  3. Embedded Models:

    • Guidelines for on-device ML deployment
    • Model optimization requirements (quantization, pruning)
    • Storage and memory constraints

Feature Engineering

  • Standardized feature transformation pipelines
  • Feature store usage guidelines
  • Feature versioning requirements

Operations

Monitoring

  1. Model Performance:

    • Required metrics for monitoring (accuracy drift, etc.)
    • Alerting thresholds
    • Visualization standards

  2. System Performance:

    • Latency monitoring requirements
    • Resource utilization standards
    • Cost monitoring guidelines

Continuous Learning

  • Guidelines for re-training frequency
  • Online learning standards (when applicable)
  • Feedback loop implementation patterns

Documentation

Model Documentation

  • Required documentation for model architecture
  • Performance characteristics documentation
  • Limitations and edge cases
  • Model cards for all production models

Integration Documentation

  • System architecture documentation requirements
  • Data flow diagrams
  • Decision boundaries and business rules integration

Ethics and Compliance

Ethical Guidelines

  • Fairness requirements across protected attributes
  • Transparency standards
  • Human oversight requirements

Compliance

  • Domain-specific regulatory compliance (healthcare, finance, etc.)
  • Data retention and privacy compliance
  • Documentation requirements for audits

Testing

Unit Testing

  • Test coverage requirements for data preprocessing
  • Feature transformation testing
  • Model wrapper/container testing

Integration Testing

  • End-to-end pipeline testing
  • Performance testing under load
  • Failure mode testing

Security

Model Security

  • Guidelines for preventing model extraction attacks
  • Adversarial robustness requirements
  • Access control for model endpoints

Data Security

  • Encryption requirements for sensitive data
  • Anonymization and aggregation guidelines
  • PII handling standards

References