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