Encord
Encord Active is an advanced active learning toolkit designed to enhance the process of building AI models. It offers various features to test, validate, and evaluate models, as well as surface, curate, and prioritize valuable data for labeling, ultimately improving model performance.
One of its key features is the ability to automatically find label errors in training data without manual inspection. By utilizing vector embeddings, AI-assisted quality metrics, and model predictions, Encord Active identifies problematic data samples and enables course correction.
Additionally, the tool introduces a unique approach to data search using natural language search. With Active, users can search and curate visual data, including images, videos, DICOM files, labels, and metadata, using only natural language.
Encord Active also allows users to debug models and enhance performance by identifying and fixing dataset errors, biases, and edge cases. It conducts model error analysis, runs automated robustness tests, and provides explainability reports to help uncover failure modes and issues.
Furthermore, the platform provides out-of-the-box metrics or the option to integrate custom metrics for detailed breakdowns of how data and labels impact models. Versioning and comparison features enable users to track progress by comparing datasets and models.
The tool further supports the creation of Active Learning pipelines that combine acquisition functions, data distribution, model confidence, and similarity search to curate high-value data that improves model performance.
Encord Active also offers integrations with secure cloud storage, MLOps tools, and other components of users’ ML pipelines, ensuring seamless workflow integration.
Overall, Encord Active is a comprehensive active learning platform that streamlines data flows, facilitates collaboration, and empowers AI teams to build reliable models with improved efficiency.
