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Model Versioning and A/B Testing Pattern

A deployment pattern that manages multiple versions of AI models simultaneously, enabling controlled rollouts, performance comparisons, and gradual migrations. This pattern supports experimentation and risk mitigation in production AI systems.

When to Use

Use this pattern when you need to test new model versions safely, compare performance between different approaches, or gradually roll out model updates. Essential for production systems where model changes need validation and when business impact needs to be measured systematically.

Architecture Drawing

The architecture illustrates a traffic routing system that directs requests to different model versions based on configured rules, with comprehensive monitoring and comparison capabilities.

Components: - User Requests (250, 50) - Traffic Router (250, 120) - Experiment Controller (400, 120) - Model A (150, 200) - Model B (250, 200) - Model C (350, 200) - Performance Monitor (400, 200) - Metrics Collector (550, 200) - Results Analyzer (400, 280) - Model Registry (100, 280)

Edges: - User Requests → Traffic Router: "Incoming Requests" - Traffic Router → Model A: "Traffic Split A" - Traffic Router → Model B: "Traffic Split B" - Traffic Router → Model C: "Traffic Split C" - Experiment Controller → Traffic Router: "Routing Rules" - Model A → Performance Monitor: "Response A" - Model B → Performance Monitor: "Response B" - Model C → Performance Monitor: "Response C" - Performance Monitor → Metrics Collector: "Performance Data" - Metrics Collector → Results Analyzer: "Aggregated Metrics" - Model Registry → Traffic Router: "Available Models"

Fine-Tuned Model Pattern, Blue-Green Deployment Pattern, Performance Monitoring Pattern

References

  1. Continuous Integration and Deployment for Machine Learning - 2020 - arXiv - Comprehensive guide to CI/CD practices specifically for machine learning systems
  2. A/B Testing for Machine Learning Models - 2018 - Uber Engineering - Real-world implementation of A/B testing infrastructure for ML models at scale
  3. MLOps: Continuous Delivery and Automation Pipelines in Machine Learning - 2021 - Google Cloud - Best practices for model versioning and automated deployment pipelines