Python · FastAPI · ML · Production Systems
Overview
Ongoing experiments in production AI pipeline design — not research, but engineering. The focus is on the infrastructure layer: how data moves, how models get served, how validation and monitoring work at production scale.
Informed by direct work integrating LLM and SLM models into production backend pipelines. The patterns here reflect real production constraints: latency, reliability, observability, and the kind of failures that don't show up in notebooks.
Areas of Exploration
Content to be filled as the project evolves. Planned: data ingestion patterns, model serving architectures, quality validation loops, monitoring and drift detection, and integration of LLM APIs into production services.
Status
Continuously active. Updated as new patterns emerge from production experience. GitHub is the live record.