129. Design an Image-Text Training Data Pipeline
Billions of image-text pairs from Common Crawl: polite fetching, CLIP scoring, dedup, recaptioning, WebDataset shards and takedowns.
Start with a template. Work through each step. Ask Coach when you need a second opinion.
Company tags are community-reported. Counts on cards show how many people reported that design.
Billions of image-text pairs from Common Crawl: polite fetching, CLIP scoring, dedup, recaptioning, WebDataset shards and takedowns.
Search by photo over a billion images: contrastive embeddings from engagement pairs, object crops, IVF-PQ with re-scoring, versioned indexes.
Captions read aloud as alt text: encoder, bridge and decoder, CLIP-filtered data, beam search, hallucination-aware evaluation, lanes on one GPU pool.
Blur every face and licence plate in billions of street panoramas: tiles for tiny faces, recall-first detection, a batch pipeline that fails closed.