Find, create, consume, and explain ML models. Git with the scale of S3.
We rely on computer vision and ML to deliver on Gather AI’s mission. XetHub has enabled our ML team to be more productive, collaborate efficiently, and iterate quickly.”
Daniel Maturana, Co-Founder and Chief ML Scientist
reduction in repository size and transfer time
data silos eliminated by switching to XetHub
cost savings over using EBS, Git LFS, and DVC
Most ML teams use GitHub for code and store their data somewhere else. It’s inefficient and error-prone, and the development process is further bogged down by slow programs and large volumes of data.
XetHub brings data and code together, enabling fast, seamless collaboration in a Git-enabled environment at scale.
XetHub can replace Git LFS, DVC, and EBS in an ML development workflow, allowing engineering teams to manage code, models, and metadata in lock-step. This speeds up model development while decreasing the risk of error.
By tracking a project’s data and code together, using Git, you have context around the model’s evolution. XetHub lets developers use familiar git and GitHub paradigms to track changes, get feedback, and explore projects.
This makes asynchronous collaboration easier (helpful for remote teams) and makes it easier for developers to explore and ramp-up on new projects fast.
Rather than having to move all your workflows to adopt a monolithic platform, XetHub works within your existing developer workflows and supports any filetype of any size.
Stay in the “flow” with free branching, data remixing, and generation of new features and aggregates - with the confidence that you won’t lose your work.