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AI Engineering Guidelines

This document outlines Bayat's standards and best practices for developing, deploying, and maintaining artificial intelligence and machine learning systems.

Table of Contents

AI Systems Architecture

System Design Principles

  • Modularity: Design AI systems as a collection of independent, replaceable components
  • Separation of Concerns: Clearly separate data processing, model training, and inference systems
  • Scalability: Design for scale from the beginning, considering both data volume and request throughput
  • Observability: Implement comprehensive logging, monitoring, and tracing at all levels
  • Reproducibility: Design systems that enable reproducible experiments and outputs
  • Graceful Degradation: Implement fallback mechanisms for when ML models fail or perform poorly

Reference Architecture

  1. Data Management Layer:

    • Data collection and ingestion pipelines
    • Data cleaning and preprocessing services
    • Feature stores for training and serving
    • Data versioning and lineage tracking

  2. Model Development Layer:

    • Experiment tracking and version control
    • Model training infrastructure
    • Hyperparameter optimization services
    • Model evaluation and validation tools

  3. Inference Layer:

    • Model serving infrastructure
    • Prediction caching and batching
    • Inference logging and monitoring
    • A/B testing framework

  4. Application Integration Layer:

    • API gateways for model access
    • Client libraries and SDKs
    • Model result post-processing
    • Application-specific adaptors

Model Development Standards

Framework Selection

  • Primary Frameworks:

    • TensorFlow or PyTorch for deep learning
    • Scikit-learn for traditional ML algorithms
    • Hugging Face Transformers for NLP models
    • Choose frameworks based on model type, team expertise, and production requirements

  • Framework Standardization:

    • Maintain consistent framework versions across environments
    • Document framework selection reasoning for each project
    • Establish migration plans for framework upgrades

Model Development Workflows

  1. Exploratory Phase:

    • Use notebooks for initial exploration with clear documentation
    • Establish baseline models and metrics
    • Document data exploration findings
    • Version control exploratory code

  2. Structured Development Phase:

    • Refactor code from notebooks into proper packages and modules
    • Implement CI/CD pipelines for model code
    • Establish model versioning strategy
    • Use standardized configuration management

  3. Production Integration Phase:

    • Convert models to production-ready format
    • Implement interface contracts for model inputs/outputs
    • Develop integration tests with consuming systems
    • Document performance characteristics and requirements

Code Quality

  • Follow language-specific coding standards (\1\2), \1\2), etc.)
  • Implement unit tests for data processing and model utilities
  • Use type hints (for Python) to improve code readability and catch errors
  • Document model architecture, hyperparameters, and expected behavior
  • Use consistent naming conventions across projects

Data Management and Governance

Data Quality Standards

  • Define and enforce data quality metrics for training data
  • Implement automated data validation pipelines
  • Document data cleaning and preprocessing steps
  • Establish minimum data quality thresholds for model training

Feature Engineering

  • Maintain a central feature registry with documentation
  • Implement feature stores for high-reuse features
  • Document feature provenance and transformation logic
  • Version control feature engineering code

Data Versioning

  • Version all datasets used for training and evaluation
  • Store data lineage information (source, transformations, etc.)
  • Use appropriate tools for dataset versioning (DVC, etc.)
  • Link model versions to corresponding data versions

Privacy and Security

  • Implement appropriate data anonymization techniques
  • Establish clear data retention policies
  • Follow secure data storage practices
  • Implement access controls for sensitive data
  • Document privacy protection measures for each dataset

Model Training and Evaluation

Training Infrastructure

  • Standardize training environment setup using containers
  • Use configuration files for all training parameters
  • Implement resource monitoring during training
  • Enable distributed training for large models

Experiment Tracking

  • Track all experiments with tools like MLflow, Weights & Biases, or TensorBoard
  • Record all hyperparameters, metrics, and artifacts
  • Tag experiments with relevant metadata
  • Make experiment results searchable and comparable

Evaluation Standards

  • Define standard evaluation metrics by problem type
  • Implement multiple evaluation approaches:
    • Offline evaluation (test sets, cross-validation)
    • Online evaluation (A/B testing)
    • Human evaluation (where applicable)
  • Benchmark against baseline models
  • Document evaluation methodology completely

Model Selection Criteria

  • Define clear acceptance criteria for models
  • Consider multiple factors:
    • Performance metrics
    • Resource requirements
    • Latency constraints
    • Fairness and bias metrics
    • Explainability requirements
  • Document model selection reasoning

Model Deployment

Deployment Patterns

  • Support multiple deployment options:
    • Container-based deployment
    • Serverless functions
    • Edge deployment
    • Batch prediction systems
  • Document deployment architecture for each model

Versioning and Release Management

  • Implement semantic versioning for models
  • Maintain model registry with versioned artifacts
  • Support model rollback mechanisms
  • Establish clear release process and approval gates
  • Document version compatibility requirements

Serving Infrastructure

  • Design for reliability and high availability
  • Implement traffic management (load balancing, rate limiting)
  • Support model ensemble deployment when appropriate
  • Provide monitoring endpoints for health checks

Deployment Validation

  • Test models in staging environment before production
  • Implement shadow deployment for high-risk models
  • Validate model behavior against expected patterns
  • Verify system performance under expected load

Model Monitoring and Maintenance

Monitoring Standards

  • Monitor model inputs for data drift and quality issues
  • Track prediction outputs for concept drift
  • Measure business KPIs affected by model performance
  • Set up alerts for anomalous behavior
  • Implement logging for all prediction requests and responses

Performance Metrics

  • Define SLAs for model serving (latency, throughput)
  • Monitor resource utilization (CPU, memory, GPU)
  • Track prediction quality over time
  • Implement custom metrics for domain-specific concerns

Maintenance Procedures

  • Establish regular retraining schedule
  • Define model update approval process
  • Implement canary deployments for model updates
  • Document model lifecycle management policies
  • Create contingency plans for model performance degradation

Responsible AI Practices

Fairness and Bias

  • Perform fairness analysis for models with human impact
  • Test model performance across different demographic groups
  • Implement fairness metrics and monitoring
  • Document known biases and mitigation strategies
  • Review data collection processes for potential bias sources

Explainability and Interpretability

  • Choose appropriately interpretable models for high-risk domains
  • Implement post-hoc explanation methods like SHAP or LIME
  • Document model limitations and uncertainty
  • Provide global and local explanations where appropriate
  • Test explanations with subject matter experts

Ethical Guidelines

  • Review all AI projects against ethical guidelines
  • Document potential societal impacts
  • Consider environmental impacts of large model training
  • Establish ethical review process for high-risk applications
  • Provide mechanisms for user feedback and appeals

Security Considerations

Model Security

  • Protect models from adversarial attacks
  • Implement input validation and sanitization
  • Test models against common adversarial examples
  • Secure model weights and parameters

Infrastructure Security

  • Follow organization security standards for all infrastructure
  • Implement access controls for model endpoints
  • Encrypt data in transit and at rest
  • Regularly audit access to model serving infrastructure

Supply Chain Security

  • Verify integrity of dependencies and frameworks
  • Review third-party models and datasets
  • Document supply chain for all model components
  • Implement vulnerability scanning for ML pipelines

Engineering Workflows

Collaboration Standards

  • Define roles and responsibilities (Data Scientist, ML Engineer, etc.)
  • Establish handoff procedures between teams
  • Use collaborative development environments
  • Maintain shared documentation

CI/CD for ML

  • Automate model testing and validation
  • Implement continuous training pipelines
  • Set up continuous delivery for model deployment
  • Version control all pipeline code and configurations

Review Processes

  • Establish code review standards for ML code
  • Implement model review checklist
  • Define sign-off requirements for model release
  • Document review findings and resolutions

Documentation Requirements

Model Documentation

  • Create Model Cards for all production models
  • Document model architecture and hyperparameters
  • Record training data characteristics
  • Document expected performance and limitations
  • Include usage guidelines and constraints

Pipeline Documentation

  • Document data pipelines and transformations
  • Create deployment architecture diagrams
  • Record environment configurations
  • Document monitoring setup and alerting thresholds

User-Facing Documentation

  • Provide clear documentation for API consumers
  • Document model outputs and interpretation
  • Include confidence scores and uncertainty metrics
  • Explain appropriate use cases and limitations

Regulatory Compliance

Compliance Requirements

  • Identify relevant regulations for different domains:
    • GDPR for personal data in Europe
    • HIPAA for healthcare applications
    • Industry-specific regulations
  • Document compliance measures for each model

Documentation and Auditing

  • Maintain audit trails for model decisions
  • Document data usage and processing
  • Prepare compliance documentation for regulatory review
  • Implement data subject rights (access, deletion, etc.)

Risk Management

  • Assess risk level for each AI application
  • Implement appropriate controls based on risk
  • Document risk assessment and mitigation strategies
  • Establish incident response procedures