Companies in the healthcare space have a particular responsibility to ensure that the technology they deploy is safe and well-tested. The current process for doing safe machine learning development, however, is far from optimal — it involves extremely meticulous manual logging of every dataset and step that went into training a model, and exactly how it was achieved.
What if there was a way for ML model development to be both fast and safe?
Dotscience takes tried and tested DevOps techniques enabling both fast and safe development, and applies them to the increasingly complex world of data and models. For example, Dotscience can track data and labels from multiple sources, and maintain a provenance trail from models that are deployed into the field all the way back through all the steps of model development and data engineering. Furthermore, collaboration is accelerated as any ML or data engineer can take another's run and fork it, then propose the changes back asynchronously, accelerating the exploration of different approaches to further increase model accuracy and efficacy.