CodaLab is an open-source web-based platform that enables researchers, developers, and data scientists to collaborate, with the goal of advancing research fields where machine learning and advanced computation is used. CodaLab helps to solve many common problems in the arena of data-oriented research through its online community where people can share worksheets and participate in competitions.
Immutable Experimentation that ensures Reproducibility
Today’s data-driven research and development is stymied by an inability for scientists and their collaborators to easily reproduce and augment one another’s experiments. CodaLab addresses this problem by providing a cloud-based virtual “workbench” where computer scientists can conduct data-driven experiments quickly and easily.
These experiments can then be easily copied, re-worked and edited by other collaborators in order to advance the state-of-the-art in data-driven research and machine learning.
Collaboration that drives Innovation
By working with the data-driven research community, we have enhanced the ability for scientists and engineers to organize and host competitions in which participants can collaborate with one another to deliver the best solutions for some of their most challenging problems.
CodaLab's community leaders include Percy Liang, of Stanford University who focuses on CodaLab Worksheets. Isabelle Guyon, of University Paris-Saclay & ChaLearn who focuses on CodaLab Competitions, and Sergio Escalera of the Autonomous University of Barcelona.