---
title: "Best_practices"
description: "Documentation for Best_practices"
---
[AIR-3][AIS-3][BPC-3][RES-3]
# AI Best Practices
## Overview
Add a brief overview of this document here.
This document outlines best practices for working with AI components in Anya Core.
## Table of Contents
- [Model Serving](#model-serving)
- [Performance Optimization](#performance-optimization)
- [Security](#security)
- [Monitoring and Logging](#monitoring-and-logging)
- [Error Handling](#error-handling)
## 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
- [Related Document](#related-document)