Databand is a system for building, running, and monitoring machine learning pipelines. Machine learning teams use Databand to manage and optimize their processes for model training, experimentation, testing, and deployment.
Ease of Use
Pipelining tools are a good solution for adding structure and reuse in data science development. However, most pipeline solutions available today are not easy to work with - a big issue in highly iterative ML work. Databand simplifies the process of building, scheduling, and understanding DAGs and pipelines so that data engineers can do it faster and data scientists are empowered to build on their own.
Databand provides deeper visibility into the flows of data running through your pipelines so that you can quickly understand what your data looks like, how it is changing, and how it will affect your models downstream.
Being able to recreate every run of a pipeline (or experiment) is critical in fast-moving machine learning teams to prevent wasted efforts. Databand automatically saves all artifacts and context from every runs so everything is totally accessible and reproducible.
Databand helps break down monolithic processes into pipelines with reusable, testable tasks and data sets, making it easier to QA your models before they go into production and have more confidence in your release.