Cleanlab is data-model and data-framework agnostic, a strong facet of its design. It doesn’t matter in the event you’re operating PyTorch, OpenAI, scikit-learn, or Tensorflow; Cleanlab can work with any classifier. It does, nevertheless, have particular workflows for frequent duties like token classification, multi-labeling, regression, picture segmentation and object detection, outlier detection, and so forth. It’s price perusing the instance set to see for your self how the method works and what outcomes you’ll be able to count on.
Snakemake
Knowledge science workflows are arduous to arrange, and that’s even tougher to do in a constant, predictable means. Snakemake was created to automate the method, establishing knowledge evaluation workflows in ways in which guarantee everybody will get the identical outcomes. Many present knowledge science initiatives depend on Snakemake. The extra shifting components you will have in your knowledge science workflow, the extra seemingly you’ll profit from automating that workflow with Snakemake.
Snakemake workflows resemble GNU Make workflows—you outline the steps of the workflow with guidelines, which specify what they soak up, what they put out, and what instructions to execute to perform that. Workflow guidelines may be multithreaded (assuming that provides them any profit), and configuration knowledge may be piped in from JSON or YAML information. It’s also possible to outline features in your workflows to rework knowledge utilized in guidelines, and write the actions taken at every step to logs.
