In the pipeline operations, the user is able to conceptually translate the results of the experimentation phase to XAI models and pipelines for production. This includes both Model Configuration and Model Management functionality.
By selecting the Pipeline Management option, the user can view the list of Explainable AI Pipelines he/she has configured while he/she may search for a specific pipeline, sort, and/or filter the results. Through the quick actions available for each pipeline, the user may edit its configuration, schedule its execution, view the execution logs (if available), visualize its results, or delete it. In the case of a new pipeline, besides the standard properties (name, etc.), the user needs to provide the input by selecting the dataset and defining the related action (forward, log), define incoming steps, the related block’s configuration and the action related to the output. Once the intermediate configuration is complete, he/she needs to add the output block from the Library so as to define how the pipeline’s results will be handed. At any moment, the user may preview or save the configuration, as well as proceed to the immediate or scheduled pipeline’s execution on the defined execution environment (local environment or cloud).