Model Deployment

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WHAT IS… ?

Model Deployment is the second step in the machine learning lifecycle that focuses on taking the model you built in the first step and deploying it in production. In this step, we focus on identifying which model should be deployed, determining how to create production versions of the code and packages, and testing, debugging, and validating your model.

     

    HOW IS IT USED ?

    Model Deployment refers to the putting in production ML models. These models may come from different libraries: TensorFlow, Keras, Pytorch or other frameworks.

       

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      Learn more by reading our installation guide and tutorials.

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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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