Encord

Encord is a data development platform that's designed to make it easier to build computer vision applications that use machine learning to predict what's in a scene and generative AI to create new imagery. It's got a range of tools to manage, clean and curate data, automate labeling processes and test models.

The platform is geared for computer vision and multimodal AI teams that want to build more accurate models faster. Among its features:

• Annotate: Supports image, video, DICOM, NifTI, synthetic-aperture radar and document data. You can use one-click automated labels, custom workflows and expert review to speed up labeling projects.
• Active: Lets you monitor, debug and test models. You can use it to find edge cases and perform robustness tests to spot data problems and model weaknesses.
• Index: Helps you manage and explore data so you can filter, search and visualize it to create a structured dataset.

Encord hopes to help improve model quality with better data management and annotation tools. The company's platform is designed to work with a range of storage and MLOps tools to keep workflows smooth. Encord also is designed to be secure, with SOC2, HIPAA and GDPR compliance.

Pricing tiers are set up to accommodate different team sizes and needs:

• Starter: Good for individuals and small teams, with image and video annotation toolkits and complex ontologies.
• Team: Good for teams building and maintaining multiple AI applications, with automation, medical and geospatial annotation toolkits, data management and model evaluation.
• Enterprise: Good for companies releasing multiple AI applications, with multiple workspaces, single sign-on (SSO), enterprise SLA and support, and private cloud and on-premise options.

Encord's interface is designed to be easy to use, and the company offers support to help customers get up to speed. For those looking to speed up their AI development lifecycle while keeping training data quality high, Encord could be a good option. If you integrate Encord into your workflow, you can cut annotation hours and speed up labeling, leading to better model performance.

Published on June 11, 2024