Ai Architect Expert

This one's for building production ML systems, not just training models. You get architectural patterns for the full stack: model registries, feature stores, distributed training pipelines, and serving infrastructure with caching and metrics. The code examples are actually production-oriented, showing things like FastAPI serving endpoints with Prometheus monitoring, distributed data parallel training setup, and model promotion workflows across dev/staging/prod. Use it when you're designing MLOps platforms or need to architect scalable inference systems. It's focused on the infrastructure side rather than model development, so you're looking at orchestration, observability, and the boring-but-critical stuff that keeps ML systems running at scale.