AI/ML Architecture
Model serving, feature stores, vector search, monitoring, and MLOps pipelines.
Model Serving
Realtime vs batch inference, autoscaling, GPU scheduling, and cost controls.
Data & Features
Feature stores, embeddings, vector DBs, retrieval strategies, and drift detection.
MLOps
Versioning, lineage, deployment gates, evaluation, and observability.