[AIR-3][AIS-3][BPC-3][RES-3]

AI Best Practices

Overview

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This document outlines best practices for working with AI components in Anya Core.

Table of Contents

Model Serving

Deployment Strategies

  • Canary Deployments
  • Gradually roll out new model versions to a subset of users
  • Monitor performance metrics before full deployment

  • Easy rollback if issues are detected

  • Blue-Green Deployments

  • Maintain two identical production environments
  • Switch traffic between environments for zero-downtime updates
  • Rollback by switching back to the previous environment

Resource Management

  • Resource Allocation
  • Set appropriate CPU/Memory limits for each model
  • Use GPU acceleration for compute-intensive models

  • Implement auto-scaling based on request load

  • Model Optimization

  • Quantize models to reduce size and improve inference speed
  • Use model pruning to remove unnecessary parameters
  • Optimize batch sizes for your hardware

Performance Optimization

Caching

  • Response Caching
  • Cache model inference results for identical inputs
  • Set appropriate TTL based on data freshness requirements
  • Invalidate cache when models are updated

Batching

  • Request Batching
  • Process multiple requests in a single batch
  • Balance between latency and throughput
  • Implement dynamic batching based on load

Security

Input Validation

  • Data Validation
  • Validate all input data types and ranges
  • Implement input sanitization
  • Set maximum input size limits

Model Security

  • Model Signing
  • Digitally sign model files
  • Verify signatures before loading models
  • Maintain a registry of trusted model hashes

Monitoring and Logging

Metrics Collection

  • System Metrics
  • CPU/Memory/GPU utilization
  • Request latency and throughput

  • Error rates and types

  • Model Metrics

  • Prediction confidence scores
  • Input/output distributions
  • Drift detection metrics

Error Handling

Graceful Degradation

  • Fallback Mechanisms
  • Implement fallback to simpler models
  • Return cached results when possible
  • Provide meaningful error messages

Retry Logic

  • Exponential Backoff
  • Implement retries with exponential backoff
  • Set maximum retry limits
  • Log all retry attempts

See Also