ML Production Routing

About

Acts as a central navigation hub for transitioning machine learning models from development to production. It provides a structured framework to categorize production concerns into four essential pillars: model optimization, serving infrastructure, MLOps tooling, and observability. By utilizing decision trees and clarifying questions, it helps developers identify whether their bottlenecks are related to model size, API architecture, workflow automation, or performance drift, ensuring they receive the precise technical guidance needed for their specific deployment scenario.

Key Features

• Decision trees to identify if bottlenecks are model-based or infrastructure-based
• 5 GitHub stars
• Guidelines for selecting serving patterns like REST, gRPC, and batch processing
• Proactive monitoring patterns for drift detection and production debugging
• Four-pillar routing framework covering optimization, serving, MLOps, and observability
• Strategies for experiment tracking, model versioning, and CI/CD automation

Use Cases

• Moving an experimental model into a scalable, containerized production environment
• Optimizing high-latency models for deployment on resource-constrained edge devices
• Establishing automated MLOps pipelines to improve team collaboration and reproducibility