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Encord is a data platform designed for computer vision teams, aiding in the development, deployment, and evaluation of predictive and generative vision applications. The platform streamlines annotation and RLHF workflows, monitors and evaluates model performance, and manages and curates data. Key Features Annotation: Encord provides a simple, powerful annotation experience to accelerate data labeling. It supports a variety of annotation types including object detection, keypoint skeleton pose, polyline annotation, instance segmentation, action recognition, frame classifications, and polygons. Workflows: The platform allows for the creation of fully customized, automated ML pipelines to improve the efficiency and quality of your annotation workforce. Model-assisted labeling: This feature allows for faster labeling with automated labeling, effectively training deep learning and fine-tuning foundation models. Quality metrics: Encord offers data-driven insights on label quality and annotator performance, to optimize workforce efficiency and ensure model excellence. Active Learning Workflows: It provides built-in active learning platform to avoid labeling errors, improve annotation quality, and diagnose model issues quicker. Integrations: Encord can be integrated with secure cloud storage, MLOps tools, and much more with dedicated integrations that slot seamlessly into your workflows. API/SDK capability: Encord’s collaborative labeling platform helps you automate annotation with AI-assisted labeling, build active learning pipelines, and streamline data operations. Use Cases Encord is used across a variety of industries such as aerospace and defense, agriculture, computer vision, energy, healthcare and medical, insurance, life sciences and biotech, logistics, manufacturing, media, gaming and entertainment, retail and e-commerce, sports, and technology and software.

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