A high-level view of the tabs within an AI Center project is as follows:

Dashboard: This tab provides an overview of the project’s status, such as the number of datasets, pipelines, packages, skills, and logs, as well as the AI Units consumption and quota.

Datasets: This tab enables you to upload, view, and manage the datasets that are used for training and evaluating the ML models within the project. A dataset is a folder of storage containing arbitrary files and sub-folders1.

Data Labeling: This tab enables you to upload raw data, annotate text data in the labeling tool (for classification or entity recognition), and use the labeled data to train ML models. It is also used by the human reviewer to re-label incorrect predictions as part of the feedback process2.

ML Packages: This tab enables you to upload, view, and manage the ML packages and package versions within the project. An ML package is a group of package versions of the same package type, and a package version is a trained model that can be deployed to a skill3.

Pipelines: This tab enables you to create, view, and manage the pipelines and pipeline runs within the project. A pipeline is a description of an ML workflow, including the functions and their order of execution, and a pipeline run is an execution of a pipeline based on code provided by the user4.

ML Skills: This tab enables you to deploy, view, and manage the ML skills within the project. An ML skill is a live deployment of a package version, which can be consumed by an RPA workflow using an ML skill activity in UiPath Studio5.

ML Logs: This tab enables you to view and filter the logs related to the project, such as the events, messages, and errors that occurred during the pipeline runs, skill deployments, and skill executions6.