Concepts

Explore Vectice's core concepts to become more familiar with how to use Vectice. Click on each concept inside the table for more detailed information.

Click on the concepts to learn more.

Concept	Description
AskAI	Quickly generate insights and documentation by leveraging AI to pull relevant information from your logged data in Vectice.
Assets	Assets are the valuable resources used during data science interations, including datasets, models, graphs, notes, code, and notebooks.
Auto-document	Automatically document descriptive information about assets in Vectice with AskAI.
Autolog	The ability to log all data science assets and code used during development using a single line of code.
Datasets	Datasets reflect the dataset metadata logged to Vectice during model development.
Iterations	An iteration is a recurring work cycle within a phase, primarily used for logging assets to document work and maintain transparency throughout an AI project.
Lineage	Lineage refers to the tracking of the origin, transformations, and relationships of assets throughout its lifecycle.
Macros	Automatically inserts predefined content and metadata from Vectice’s logged information to speed up documentation.
Models	Models reflect the model metadata logged to Vectice during model development. All models are versioned and tagged with their deployment environment within Vectice.
Phases	Phases help organize project objectives, ensure best practices, maintain consistency, and document knowledge.
Projects	Projects enable data science leaders to oversee team workflows and track data science projects.
Reports	Reports reflect the auto-generated reports using information and assets from the selected iteration.
Workspaces	Workspaces help organize projects and members in an organization, simplifying collaboration and permission management.