Encord

What is Encord?

Encord is a data development platform designed to support AI applications by providing tools for data curation, labeling, and model evaluation. It offers features such as efficient labeling tools, customizable workflows, multimodal annotation capabilities, model performance evaluation, and integrations with cloud storage and MLOps tools. Encord was founded by individuals with backgrounds in quants, physics, and computer science who identified the need for purpose-built tools to facilitate the practical development of AI applications. The company has a team with diverse technical expertise and is supported by investors and industry leaders.

Encord Active is an advanced learning toolkit within Encord that enhances the AI model building process. It automates the detection of label errors in training data using vector embeddings, AI-assisted quality metrics, and model predictions. This toolkit helps in refining the model by surfacing and curating valuable data for labeling, debugging models, identifying dataset errors and biases, conducting model error analysis, and running automated robustness tests. Encord Active also offers features for data prioritization, custom metric integration, versioning, and comparison of datasets and models.

Who created Encord?

Encord was created by former quants, physicists, and computer scientists who aimed to address the lack of purpose-built tools and infrastructure hindering practical AI application development. The company was launched on June 20, 2024. The founders believed in a better way to make AI development practical from the outset. The Encord team comprises individuals with deep technical backgrounds and diverse operating experience from major technology companies. They are backed by prominent leaders in the industry, including CRV, Y Combinator Continuity, and other notable Bay Area investors.

What is Encord used for?

• Seamless workflow integration
• Automated robustness tests
• Supports visual data search
• Model error analysis
• Cloud storage integration
• Customizable metrics integration
• Creates Active Learning pipelines
• Natural language search for data
• Debugging and performance enhancement capabilities
• Detailed dataset impact breakdown
• Efficiently label data of any modality including image, video, medical imagery, or geospatial data and audio
• Monitor team and annotator performance with actionable dashboards to ensure training data quality
• Seamlessly control user roles with permissioning, manage task assignment, and infinitely scale MLOps workflows
• Identify edge cases and underrepresented classes and conduct robustness and regression tests to uncover failure modes and issues in models
• Deconstruct model performance with automatic reporting on metrics like mAP, mAR, and F1 Score
• Integrate humans-in-the-loop to build active learning workflows to refine model performance iteratively, significantly reducing deployment timelines
• Connect secure cloud storage, MLOps tools, and more with dedicated integrations
• Offer developer-friendly API/SDK for easy access
• Aid in finding label errors in training data using vector embeddings, AI-assisted quality metrics, and model predictions
• Automate the process of finding label errors in training data without manual inspection
• Integrate humans-in-the-loop to build active learning workflows to refine model performance iteratively
• Natural language search for data, debugging, and performance enhancement capabilities
• Detailed dataset impact breakdown and customizable metrics integration
• Versioning and comparison features
• Visually identify and inspect data outliers with embedding plots
• Build balanced and representative datasets by identifying underrepresented visual quality metrics
• Accelerate labeling projects at scale with custom or state-of-the-art foundational models
• Ensure reliable quality assurance with customizable workflows
• Monitor team performance with actionable dashboards to ensure data quality
• Collaborate with teams of all sizes by controlling user roles and managing task assignment
• Identify edge cases and underrepresented classes for robustness and regression tests
• Integrate humans-in-the-loop to refine model performance iteratively

Who is Encord for?

• Engineers
• Operatives
• Creatives
• PhDs
• Clinical AI Specialist
• Head of CV
• Neuradiologist
• VP of Data Science
• Operations Director
• Data Science Professional
• Computer Vision Expert
• Operations Manager
• ML practitioners
• Computer vision & multimodal AI teams
• ML Solutions Engineer
• CS Manager
• Data Scientist
• Computer scientist
• AI Practitioner

How to use Encord?

To use Encord, follow these steps:

1. Sign up for an account on the Encord platform.
2. Access the Data Engine to manage, clean, and curate your data efficiently.
3. Utilize the annotation tool Annotate to label data accurately, leveraging custom or pre-built models for faster annotation.
4. Ensure quality assurance with customizable workflows and expert reviews.
5. Explore the Active Learning toolkit for advanced features like automatic label error detection and model performance enhancement.
6. Take advantage of Encord's secure platform, compliant with SOC2, HIPAA, and GDPR standards.
7. Monitor team performance, collaborate seamlessly, and fix data issues easily with actionable dashboards.

Encord offers different support plans (Starter, Team, Enterprise) to suit varying needs, providing features like AI-assisted labeling, model evaluation, RBAC, and collaboration tools.