Data Validation

#Terms

WHAT IS… ?

When data is used to train a new model, it is important to ensure that the data is valid. Data validation means checking the quality of source data before using it for training models. It ensures that anomalies that are infrequent or manifested in incremental data are not silently ignored. It focuses on checking that the statistics of the new data are as expected (e.g. type of statistical distribution and its parameters)

     

    HOW IS IT USED ?

    Data Validation is done in order to ensure that data-based systems are working as expected.

       

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      FAQS / Any question?

      Is there any current solution on the market that is as efficient as Shapelets?

      There are fantastic tools out there, no doubt! But these tools are not integrated with your analytical workflows, security systems, web apps infrastructure, etc. When working with Shapelets, you'll get that feeling of self-integration. A platform where you can run all your workflows efficiently.

      What is the licensing model for Shapelets?

      The license model is very simple. Shapelets comes as a built-in box, it is based on the amount of resources you are powering in the platform. It is not based on the number of users, or the volume of data you put into the system.

      Regarding data visualization, can I share my project with third parties?

      Yes, you can share your project with third parties. Additionally, you can create different users and edit the permissions for your team to edit, create or share a project.

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