Stop Abusing Git for ML Data: The Architect’s Guide to AI Storage

For machine learning projects with large, frequently changing datasets, Git and Git LFS can create slow clones, inefficient CI/CD workflows, and poor visibility into binary-file changes, making them better suited to code, configuration, and potentially finalized data than in-progress training data. The discussion contrasts local POSIX file systems, which provide fast low-latency development and GPU scratch storage but do not scale well for collaboration, with S3-style object storage, which offers scale but can be operationally separate from development environments and costly or cumbersome to update and transfer. It presents Hugging Face Buckets as an S3-compatible alternative for mutable ML artifacts, intermediate datasets, checkpoints, logs, and Parquet files, using the Xet content-addressable backend to deduplicate changed blocks and reduce transfers during iterative work. The recommended approach is to use local storage for active compute tasks, Git for source and configuration, and dedicated mutable object storage for evolving ML data, then publish finalized datasets to versioned repositories if needed.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.