Encord

What is Encord?

Encord is a data-focused platform for AI development that covers data curation, labeling, and model evaluation. It supports annotation for images, videos, medical scans, geospatial data, and audio, with customizable workflows and integrations with cloud storage and other MLOps tools.

A core component of the platform is Encord Active, a toolkit for improving the model-building process. It automatically finds errors in training data using vector embeddings, quality metrics, and model predictions. You can use it to curate valuable data for labeling, debug models, detect issues and biases in datasets, analyze model errors, and run robustness tests. It also supports data prioritization, custom metrics, and versioning of data and models with side-by-side comparison.

Who created Encord?

Encord was founded by former quants, physicists, and computer scientists who saw that building AI applications in practice was held back by a lack of specialized tools and infrastructure. The team has deep technical experience, including work at major tech companies, and the company is backed by investors including CRV and Y Combinator Continuity, along with other prominent Bay Area firms.

What is Encord used for?

• Data labeling: Annotate images, videos, medical scans, geospatial data, and audio.
• Model error analysis: Understand why models make mistakes, with dataset impact breakdowns showing how data affects performance.
• Edge case discovery: Identify tricky situations and underrepresented data classes, then test where models might fail.
• Automated robustness tests: Check how models handle different situations.
• Visual and natural language search: Find data visually or by typing what you’re looking for.
• Annotation monitoring: Track team and annotator performance through dashboards to maintain training data quality.
• Active learning pipelines: Build pipelines that improve over time, with custom metrics for measuring performance.
• MLOps scaling: Manage user roles, assign tasks, and scale workflows.

Key takeaway

Encord consolidates data curation, labeling, and model evaluation in one platform. Its strength lies in finding errors and biases in training data automatically, making it useful for teams that need to improve model quality without building custom tooling.