dat VS dvc

I'm surprised you didn't include things like `dat`[0]

[0] https://github.com/dat-ecosystem-archive/dat

Gun is an opinionated high level and complete solution, it may provide more than what you need. I would think a more modular alternative is suitable, something like using libp2p. Anyway, you should take a look at the dat ecosystem and the ipfs ecosystem. Also, a really neat and clever way to simply do distributed peer discovery, removing the need for a signalling server, is bitboot (it uses mainline DHT).

Dat/Hypercore lets people share data with others, and you can build applications on top of it

Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.

What you’re describing sounds like DVC (at a higher-ish—80%-solution level).

https://dvc.org/

See pachyderm too.

10. DVC by Iterative | Github | tutorial

Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.

I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.

DVC (Data Version Control):

I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.