Glossary

An Annotation entity refers to a Label, tag, or Metadata associated with an Item in a Dataset. Annotations are used to provide additional context or information about an Item, and to enable machine learning models to understand and interpret the data. Annotations are typically created by human annotators, who use the UI to draw bounding boxes, select Labels, or enter text.

Functions and services can be deployed to execute automated processes on any entity within the Dataloop platform, including data items, annotations, tasks, datasets, and projects.

A Feature Vector or Set is a numerical representation of an object or entity, typically used in machine learning and data analysis. It consists of a list of Features or Attributes that quantitatively describe the object. A Feature set, on the other hand, is a collection of Feature Vectors that are used to train a machine-learning algorithm. The Feature Set contains all the necessary Features that are relevant to the problem being solved.

An Item in Dataloop is a unit of data that represents a ‘single instance’ or ‘file’ of a larger Dataset. It can be an image, a video, a sound recording, a text document, or any other type of digital asset that needs to be labeled, annotated, or analyzed. Each Item in the Dataloop system is typically associated with one or more Tasks, which define the specific operations that need to be performed on the Item.

When a worker finishes working on an Item in their Assignment, they act to set a status for the Item. For example, complete an action on an Item so it will have "Completed" status. Users can customize their status and use it later as a trigger event, analysis report, or search and filter activity.

Labeling is formalized within a project by creating a labeling task for an entire dataset or specific items. Items completed by annotators (with a current status of "Completed") can then be assigned to a QA task for approval (status of "Approved"). If errors are found, an issue is assigned to the annotation, sending it back to the original annotator for correction. Once corrected, the annotation is reassigned for review. If the correction is satisfactory, it is marked as "Approved."

A Model (architecture) entity in Dataloop refers to a machine learning algorithm that has been trained on labeled data to make predictions or perform other Tasks. Models are a key component of the Dataloop system, as they enable users to apply machine-learning techniques to a wide range of data types, including images, text, and audio. A Dataloop Model is a combination of data configurations and code to represent a learnable instance of the data.

A python class to wrap a generic ML code (train, predict, etc.) for standardizing models (and frameworks) to match the requirements of the Dataloop API. It can contain Log Sample to measure and compare different models and metrics. e.g. saving the training/validation loss and accuracy of a training session.

A baseline version created from a model architecture in the AI Library (usually untrained, and then starts training on selected data), or from an existing Model Version.

A collection of functions (machine processing via FaaS, models and code snippets) and tasks (human processing in labeling and QA tasks) that creates a processing flow. Structured from nodes (processing units) and connections (to move data types between nodes).

A Recipe is a set of instructions or rules that define how data must be processed, labeled, or analyzed within a Project. It can be templates or workflows that provide a standardized way of working with data and can help to streamline the process of generating labeled Datasets for machine learning and other applications. Linked with an Ontology, the Recipe adds labeling instructions and settings, such as labeling tools, mapping of tools to specific labels/Attributes, PDF instructions file, etc.

A serverless computing Service that allows users to run code without the need to manage servers or infrastructure. It can also be thought of as a deployed Package that serves the code. Given the matching input to a function, it will run and return the output.

A unit of work that is completed by an individual or a team. It can be annotating data, reviewing Annotations, labeling images, performing quality assurance checks, or any other data-related Task that requires human input. Tasks are created by managers, who define the requirements for each Task, such as the data type to be labeled, Annotation instructions, due date, priority, and the number of annotators required. Tasks are then assigned to individual annotators or teams of annotators.

A rule-based mechanism that initiates an action when a specific event occurs. Triggers are used to automate workflows and streamline data processing. They are created by defining a set of conditions that must be met for the Trigger to be activated. These conditions can be based on a variety of factors, such as the content of data, the time of day, or the occurrence of specific events. See ‘Event’ and ‘Cron’ trigger types.

Users are added to the project either from the organization that owns the project (typically for higher-level roles) or from other organizations (or possibly no organization at all). A project can have one or more Project Owners and Developers. The Project Owner can add Annotation Managers, who oversee the day-to-day work of all Annotators.