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Encord is a platform for data-centric computer vision.

Machine learning and data operations teams use Encord's collaborative applications, automation features, and APIs to build models & annotate, manage, and evaluate training datasets for computer vision.

Encord allows you to:

  • Rapidly create training data & models while retaining 100% control of your data and processes
  • Easily migrate from in-house tools & build custom model & data pipelines within minutes
  • Deliver value from your AI initiatives faster

Encord is available as a web application, Python SDK or REST API. Most of the documentation here is concerned with navigating and using the web application.

For documentation concerning the SDK or API, please see the SDK Documentation (external link) or the API section respectively.


Encord's collaborative annotation tools help you orchestrate & customize labeling workflows and automatically label data. Annotate images, video, medical imagery and geospatial data - all in one platform.

Learn more about Annotate.


Encord allows you to build models for automated data labeling that target individual features in your ontology in an easy-to-use interface. We call these micro-models. Rapidly train micro-models using Encord's GPU-accelerated container orchestration system on sparse amounts of data.

Learn more about applying automation using training and inference.


Machine learning teams use Encord to visualize and validate model predictions in active learning pipelines using our web app, Python SDK & APIs.


Our Evaluate documentation is under construction. Please check back here soon!


Machine learning and data operations teams use Encord for label and data management. Integrate private cloud buckets and existing data pipelines into Encord's system to organize all your internal processes. Version and tag projects and labels to experiment rapidly and get to the proper data process quicker.


Our Manage documentation is under construction. Please check back here soon!