AI Model Management

After configuring the model details, selecting the Object and Source, and defining AI settings, the AI model is created and appears in the Data Classification Recommendation List View.

Users can manage and organize Recommendation Models:

• Model List: Users can browse a list of all recommendation models. Each model displays key details like domain, object type, schedule, current status, last run details, and creator information.

• Favourites: Users can mark frequently used models as favourites for easy access. This personalizes the model list, keeping the most relevant models at the top.

• Recommendations: Users can see the number of recommended terms for each data object within a selected model. This count reflects user actions (accepted/rejected recommendations).

• Status: The current status of each recommendation model

  • New: Newly created model.\

  • In Progress: The model is currently running recommendations.

  • Finished: Recommendations completed successfully.

  • Failed: An error occurred during execution.

    • If the model is not configured correctly or the configuration is incomplete, the process will fail when the user tries to run the model.

    • If unexpected data issues occur during the model's execution, the status will be marked as failed.

• Domain: The specific domain the model uses for recommendations (e.g., Finance, Healthcare). Terms from the domain would get recommendations.

• Last Run: Date and time the model last ran, recommendations.

• Duration: Time taken for the last recommendation run.

• Created By: The User who created the model.

• Created Date: Date and time the model was created.

User Actions

• Run Model: Users can initiate a model to generate new recommendations for their data objects.

• Schedule Model: Users can set a specific time or a recurring schedule for a model to run automatically.

  • Scheduling automates the execution of an AI Model at a specific time or regularly. By setting up a schedule, users can avoid manual execution each time.

  • When the scheduled job runs, observers and experts receive notifications regarding its success, failure, or partial success.

  • For example, if an AI Model is scheduled to run monthly on the 5th day at 02:10 hours, the job will execute accordingly, updating existing data as needed.

• Edit Model: Users can modify existing models to configure and select data. Every step can be changed and refactored. The updated changes can be saved, and the model can be executed again.

• Delete Model: Users can remove models that are no longer needed.

AI Model Summary

In the Data Classification Recommendations list view, clicking an AI model name shows a breakdown of recommendations for data objects within the chosen connector or schema.

Each data object's details are displayed, along with suggested terms and a score indicating how likely they are to be correct. Users can then accept (thumbs up) or reject (thumbs down) these suggestions. They can also see the data object's name, content, and score based on patterns to gain more context.

When the AI model is executed for all data objects, it provides results for objects with and without term associations. These results are displayed in the Current Terms column. If the user accepts the recommended terms, a job is triggered to replace the current terms with the recommended terms.

Example: In a database, the Sales Data schema contains two tables:

1. Customer Transactions: Associated with the term "Retail Data."

2. Product Listings: No term is currently associated.

When the AI model is executed, it analyzes the data objects and recommends the term "E-commerce Data" for both tables. If the user accepts this recommendation:

• The Customer Transactions table's term "Retail Data" will be replaced with "E-commerce Data."

• The Product Listings table, which previously had no term, will now be associated with "E-commerce Data."

This ensures that the recommended terms are consistently applied across the relevant data objects.

Existing classifications for the terms are included for reference. If the suggested terms aren't right, users can add the correct term, ensuring the data is classified accurately.

User Actions

• Bulk Operations: Users can bulk approve or reject recommendations.

• Restoring the Rejected Recommendation: Users can restore recommendations that were mistakenly rejected, either individually or in bulk.

  • Users can give thumbs-up or thumbs-down for recommendations. If a user mistakenly thumbs down a recommended term, they can restore it using the "Restore Rejected Recommendation" option. The Restore Rejected Recommendations feature displays all rejected recommendations, allowing users to restore them individually or in bulk.

    

A mechanism to restore rejected recommendations eliminates the need to rerun AI models or algorithms to generate new recommendations. This ensures that user feedback is accurately reflected and maintains the quality of the recommendations.

• Run Recommendation Model: Users can run the model to generate new recommendations for the data object.

• Schedule Recommendation Model: Users can schedule a model to run regularly at specific time intervals.

Scheduling automates repetitive tasks, ensuring processes run consistently and promptly without manual intervention.

How the AI Model will help:

A user needs a connector to crawl every first day of the week to get recommendations for newly crawled data objects. By scheduling the entire workflow, the user can automate this process. The scheduled job will first trigger the connector crawling and then execute the AI model. This way, every first day of the week, the user will receive delta recommendations for newly crawled objects, ensuring they always work with the most current data.

• Edit Recommendation Model: Users can edit and save changes to the model configuration. Every step can be changed and refactored. The updated changes can be saved, and the model can be executed again.

• Delete Recommendation Model: Users can remove models that are no longer in use.

• Model Metrics: Provides an overview of model performance and details of recommendation acceptance/rejection.

AI Model Metrics

Model metrics provide valuable insights into how a user's recommendation model is functioning. This information enables users to evaluate the model's performance and make informed decisions.

• Users can view details about each model execution, including timestamps.

• Users can track the number of recommendations accepted and rejected after each run.

• Users can determine if the model needs adjustments based on the metrics to improve its recommendation accuracy.

• Users can choose to delete a model if it consistently produces irrelevant recommendations or is no longer needed.