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Version: docs v25.02

Mellerikat for Splunk User Manual

Sequence of Progression:

  1. Initial Setup
  2. Register AI Solution
  3. Select Train Data
  4. Create Dataset, Train, and Deploy Model
  5. Perform Inference


1. Initial Setup

  1. Go to 'All configurations' in Splunk Enterprise and configure the web addresses of AI Conductor and Edge Conductor.

  1. Select the 'Path Configuration' tab and set the path where data will be stored and loaded from.

2. Register AI Solution

  1. Move to the 'Create AI Solution' tab and save the splunk.yaml file according to the guide.

  1. Check the registered AI Solution in the AI Conductor.

3. Train 데이터 선택

  1. Check the data for model training.

  1. Enter the file name to be saved and click the Send button to transfer the data to the specified path.

4. Create Dataset, Train, and Deploy Model

  1. Access the Edge Conductor, go to the Dataset tab, and click the New Dataset button.

  1. Enter the necessary information for the Dataset and click the Next button.
  • Dataset Name: Name of the Dataset to be created * required
  • Description: Description of the Dataset to be created
  • Tag: Tag information to be assigned to the Dataset
  • Solution: Name of the AI Solution to be linked with the Dataset * required
  • Data Source: Type of the path from which the Dataset will be obtained * required

  1. Enter the S3 path configured in the Configuration tab, click the Import button to import the data, and then click the Save button to save the Dataset.

  1. Move to the Streams tab and click the New Stream button.

  1. Select the created AI Solution.

  1. Enter the necessary settings for training and complete the Stream creation.
  • Stream Name: Name of the Stream to be created * required
  • Description: Description of the Stream to be created
  • Tag: Tag information to be assigned to the Stream
  • AI Solution: Set Parameters for training
  • Inference Warning Setting: Set the lower limit for model performance warnings

  1. Click the Train button on the created Stream, select the Dataset to be used, and proceed with the training.

  1. Once training is complete, deploy the model to the installed Edge App.

  1. Verify that the model is successfully deployed and the Stream status is "Deployed."

5. Perform Inference

  1. Go to the Inference tab and check the data for performing inference.

  1. Click the Request Inference button to check the inferred result values.

  1. Check the inferred result values in the Inference Detail section. You can view the Inference Detail in two ways:
  • View overall summary

  • View individual results