Using ML Production

Overview

This meta-skill routes you to the right production deployment skill based on your concern. Load this when you need to move ML models to production but aren't sure which specific aspect to address.

Core Principle: Production concerns fall into four categories. Identify the concern first, then route to the appropriate skill. Tools and infrastructure choices are implementation details, not routing criteria.

When to Use

Load this skill when:

• Deploying ML models to production
• Optimizing model inference (speed, size, cost)
• Setting up MLOps workflows (tracking, automation, CI/CD)
• Monitoring or debugging production models
• User mentions: "production", "deploy", "serve model", "MLOps", "monitoring", "optimize inference"

Don't use for: Training optimization (use training-optimization), model architecture selection (use neural-architectures), PyTorch infrastructure (use pytorch-engineering)