About AI algorithms

AI algorithms analyze different kinds of data and produce AI models, which automate research, reducing the need for manual work in solving problems. These unsupervised algorithms enable the use of unlabeled data for training AI models without any human intervention. IBM Cloud Pak® for AIOps training generates models for a number of different AI algorithms. You can train the models for each of these AI algorithms independently of each other.

You use the AI Model Management to set up training for various AI algorithms.

The AI Model Management has these tabs: Training and Data assets.

  1. The Training tab allows you to view two types of algorithms in tile format:

    • Trainable AI algorithms These algorithms must be trained on certain data types before they can create deployable models. They include change risk, log anomaly detection-natural language, metric anomaly detection, similar tickets, and temporal grouping. These algorithms are listed as tiles. You can set up the training for each algorithm separately.

    • Pre-trained AI algorithms These algorithms don’t require models to derive insights. They are enabled by default. These algorithms include probable cause, scope-based grouping, topological grouping, and log anomaly detection-statistical baseline. Again each of these algorithms is represented by a tile.

  2. The Data assets tab shows the data integrations that are feeding your AI training. It lists them in a table and gives details about each integration, such as Integration name, Data flow status (Running or Finished), Integration type, Data type, and Data collected (Live or Historical).

Note: It is worth noting that naming the training is an optional step in setting up training for trainable AI algorithms.

For more information about AI algorithms, see the following topics: