This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the RadarUnderstand more
TrialWorth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.
In 2018 we mentioned DVC in conjunction with the versioning data for reproducible analytics. Since then it has become a favorite tool for managing experiments in machine learning (ML) projects. Since it's based on Git, DVC is a familiar environment for software developers to bring their engineering practices to ML practice. Because it versions the code that processes data along with the data itself and tracks stages in a pipeline, it helps bring order to the modeling activities without interrupting the analysts’ flow.