The Collaborative Training Data Platform for Sports Analytics

From data annotation to assessing model performance, Encord provides the end to end solution for AI Teams in Sports Analytics looking to get their models into production faster and turn their data into AI.

Trusted by pioneering AI teams

Automated Match Analytics

AI-Assisted Referee

Athlete Identification and Classification

Supporting AI teams like yours

Problem

Reliance on in-house tool and interface for data annotation was time consuming and single-purpose

Solution

Leveraged Encord’s multi-frame object detection, collaboration features and API

Results

Flexible annotation tools allowed the organization to move at speed to create high quality training data

Simplify Annotations With a Wide Range of Tools

Leverage Encord’s advanced labeling toolkit to ensure precise annotations in a range of modalities such as image, video and more.

Object Detection

Keypoint Skeleton Pose

Polyline Annotation

Hanging Protocols

Instance Segmentation

Action Recognition

Frame Classifications

Polygons

Automate Annotation at Speed

Automated object tracking, interpolation and Encord’s in-platform foundation models for automated labeling allow for faster annotation than ever before in fast-paced scenarios.

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Expedite Model Production With Active Learning

Train, test, and deploy models in automated pipelines whilst ensuring your model is being trained on the most informative data points using Active Learning techniques.

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Build better models faster with Encord

Built With Security in Mind

Encord is SOC2, HIPAA, and GDPR compliant with robust security and encryption standards

Pre-Built Integrations

Import data from your desired storage bucket such as AWS, Azure, Google Cloud Storage & Open Telekom Cloud OSS.

Encord API & SDK

Programatically access projects, datasets & labels within the platform via API.

Software To Help You Turn Your Data Into AI

With Encord's collaborative annotation platform you can get to production AI faster while meeting regulatory requirements. Quickly label large training datasets from all modalities, and leverage foundation models to speed up your development process.