AI infrastructurefactoriesDeep Learning
How to Build an AI ‘Factory’ for Deep Learning Models: A Python Pipeline for Scalable Training and Deployment
9 min read · 1,668 wordsBy Orandi Felix
Don’t use this verbatim: if your static data is > 100 GB, streaming JSON line-by-line will bottleneck. Switch to Parquet + fsspec + arrow + random access. I’ll show a chunked Parquet version at the end.
I chose Hydra because it keeps nested configs readable, supports remote overrides, and has a **first-class Python API**. Alternatives: `configurable` (Meta’s internal), `gin-config` (Google), `omegaconf` alone.
Jaeger UI will now show traces like: ``` [ai-factory] training-job → batch=64 → duration=74.32s → checkpoint_epoch=24 → upload_artifact → deployment ```