AI Data Pipeline 101: Ingest & Archive

This post elaborates on the AI Data Pipeline Ingest and Archive capability by detailing the five key phases of the AI data pipeline for model training and emphasizing the integral role of storage infrastructure. It specifically focuses on the nuances of data ingest, differentiating it from data collection, and highlights the requirements of streaming ingestion for AI (low-latency access, high rate limits, high throughput). The post provides concrete guidance on implementing taxonomy with examples, retaining metadata through object tags and sidecar files, and exposing metadata to training pipelines via manifests. It also offers a more detailed breakdown of storage tiering decisions based on ingest type (batch, streaming, hybrid) and Backblaze B2's pricing advantages for AI workloads.