AI & ML architecture

AI and ML architecture covers how models are trained, served, monitored and governed inside real systems. With large language models, much of the work has shifted from training models to orchestrating them: retrieving the right context, controlling cost and latency, and keeping outputs safe and auditable.

The question this category answers

How do we get model-powered features into production so that they stay accurate, affordable and accountable?

Topics

• Retrieval-augmented generation (RAG)
• AI agents and tool use

Coming soon

• Model gateways and routing
• MLOps and LLMOps
• Evaluation and monitoring of model output
• Human-in-the-loop approval
• AI governance and audit trails