We will be updating this article quarterly and tracking all the position changes, feature releases, large news, and announcements. Project and quality management ( Team/role management, annotation project management, and performance tracking).MLOps and Automation ( SDK, webhooks, orchestrations, AI-enabled labeling, model management, etc.).Integrations and security (Storage integrations, model inference API or model training integrations, etc.).AI Data Management and curation (active learning, query system, smart sampling, subset selection, data versioning, etc.).Annotation software ( image annotation, video annotation, text annotation, etc.).As we indicated above, for each company/solution, we will cover the following: We will rely on one of the most reliable software ranking marketplaces, G2, where data labeling is a separate software category within various AI software. We will cover various types of data labeling companies, along with their history and functionalities, detailed feature sets, additional AI pipeline-oriented components, and much more. Therefore, this article is neither about simple data labeling tools using open-source software such as label studio, cvat, labelme, nor is it about specific functionalities within labeling editors such as bounding boxes, polygons, text labeling, etc. Namely, annotation software, AI data management and curation, integrations and security, project and quality management, and automation. We identified 6 essential components that make data labeling tools a compelling solution for building modern AI pipelines. However, data labeling tools or simple data labeling editors are far away from covering all the growing needs of anyone's complex ML pipeline. Since ML engineers are spending such a huge portion of their time on structuring, labeling, versioning, and debugging datasets to become AI-ready training data (aka SuperData), data labeling toolsets have become essential for building scalable AI applications.
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