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ML Models

ML Models

A Model is an algorithm used to find patterns in data without the programmer having to explicitly program those patterns. The term ML Model refers to the model artifact that is created by the training process. You can use the ML model to get predictions on new data for which you do not know the target. For Example- A chat bot is an ML model which may comprise of a Neural Net to interpret speech and convert it into text and another statistical model to filter the keywords of the converted speech query.

  1. To access ML Model, Click on Machine Learning tab on the left navigation bar.

2. Click ML Models.

3. To add ML Models, Click Add Model button.

  1. In the appeared form, select specific AI Usecase for which you are planning to work, select Data Source for the related ML Model, In item type select the type of model you want to create. Select item name in which you have to write the name of the relevant ML Model.Then you need to select the assignee, who will approve your ML Model.

After creating ML Model, it will go to Task Pending of the assigned Approver. The assigned Approver has to go to his list of Task Pending for Approving or Rejecting the request. Both the Assignor and the Approver can track the status of the same task from their respective Task Pending page.

Below are the details of the process step by step -

  • Approver firstly needs to click on the specific Task Name.Here Approver will get the details related to task in the Task Brief, he can also see the relevant documents attached for that usecase and can also comment on the task.
  • After that, Approver can click the Approve button.Then Approver will need to fill the form appeared. He can also assign the ML Model to other team member.
  • Then Approver can click on Approve button if he wants to approve. To reject, he can click on Reject button. He can also click on Request to Probyto, if he wants Probyto to work on that task.

Then Select the Due date, it is a kind of expiry date of your data source. Then click on Create button.

5. After adding ML Models, you will see all the ML models you have added on the home screen of ML models with its Usecase ID, type, Last edited by, type, activation status etc. as shown in the below picture.

6. To edit or configure ML Model, Click on the name of any ML Model.

7. On the second Level page of ML Model You will find Configure, Click on Configure.

Fill all the details such as-

  • Enter the Key- In this column, you need to provide configurations for successful integrations.
  • Enter the Value-In this option, you need to give the detail or the link related to your Key.

Then click Submit.
After adding all the configurations, it will be shown below the description of MLModel.

You can also edit your ML Models details, To edit ML Models, Click Edit Model.
Fill all the relevant details, click Update.