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

Model Management

Overview

Add a brief overview of this document here.

Table of Contents

This document outlines the model management system in Anya Core's ML infrastructure.

Model Registry

Registering a New Model

from anya_ml import ModelRegistry

registry = ModelRegistry()
model_id = registry.register_model(
    name="sentiment-analysis",
    version="1.0.0",
    framework="pytorch",
    path="/path/to/model.pth",
    metrics={"accuracy": 0.95, "f1": 0.92}
)

Model Versioning

Models follow semantic versioning (MAJOR.MINOR.PATCH): - MAJOR: Breaking changes - MINOR: New features, backward compatible - PATCH: Bug fixes and patches

Model Serving

Starting a Model Server

anya-ml serve --model-id sentiment-analysis:1.0.0 --port 8080

Making Predictions

import requests

response = requests.post(
    "http://localhost:8080/predict",
    json={"text": "Anya Core is amazing!"}
)
print(response.json())

Model Monitoring

Metrics Collection

Key metrics are automatically collected: - Prediction latency - Throughput - Error rates - Resource usage

Alerting

Configure alerts for: - High prediction latency - Increased error rates - Model drift - Resource constraints

Model Updates

Rolling Updates

# Start canary deployment
anya-ml update --model-id sentiment-analysis:2.0.0 --strategy canary --percentage 10

# Monitor canary performance
anya-ml monitor --model-id sentiment-analysis:2.0.0

# Complete rollout
anya-ml update --model-id sentiment-analysis:2.0.0 --strategy rolling --batch-size 20%

Rollback Procedure

# Check rollback targets
anya-ml history --model-id sentiment-analysis

# Rollback to previous version
anya-ml rollback --model-id sentiment-analysis --to-version 1.0.0

Model Security

Access Control

# .anya/model_permissions.yaml
models:
  sentiment-analysis:
    read:
      - team:ml
    write:
      - user:admin
    admin:
      - user:ml-admin

Model Signing

All models are cryptographically signed:

# Sign a model
anya-ml sign --model-id sentiment-analysis:1.0.0 --key ~/.keys/private.pem

# Verify model signature
anya-ml verify --model-id sentiment-analysis:1.0.0 --key ~/.keys/public.pem

Best Practices

Model Packaging

  1. Include all dependencies in requirements.txt
  2. Provide example input/output in examples/
  3. Document model architecture in README.md
  4. Include evaluation metrics and test results

Performance Optimization

  • Use ONNX for cross-framework optimization
  • Enable model quantization for inference
  • Utilize hardware acceleration (CUDA, MPS, etc.)
  • Implement request batching

Troubleshooting

Common Issues

  1. Model Loading Failures ```bash # Check model format file /path/to/model

# Verify dependencies pip freeze | grep -E 'torch|tensorflow|onnx' ```

  1. Performance Issues bash # Profile model anya-ml profile --model-id sentiment-analysis:1.0.0 --input /path/to/test_data.json

  2. Permission Errors ```bash # Check model permissions ls -l /path/to/model

# Verify API keys anya-ml config list ```

Getting Help

See Also