Scale AI data processing with Hugging Face HF + Dask on Coiled for TB-scale pipelines

AI Impact Summary

The document outlines a scalable pipeline for AI data processing using Hugging Face datasets, a FineWeb-edu classifier, Dask for distributed computation, and Coiled to deploy on cloud GPUs. It demonstrates moving from local Rust-like tests toTB-scale workloads by streaming Parquet data, applying a multi-GPU text-classification model in parallel, and writing results back to Parquet with distributed guarantees. This reveals practical pathways and configuration knobs (batch size, map_partitions, meta) to achieve high throughput on large catalogs like the FineWeb dataset, highlighting the coupling of Hugging Face models with Dask for out-of-core processing. Operationally, it underscores the need for cloud GPU provisioning, environment synchronization, and workflow orchestration when scaling to hundreds of millions of rows.