Encord is an advanced platform designed to significantly enhance the accuracy and efficiency of machine learning (ML) models, particularly in computer vision.

It provides a comprehensive suite of data curation, labeling, and model evaluation tools — helping ML practitioners confidently deploy production-ready AI applications.

By leveraging Encord, users can enjoy many benefits, including dramatically increased edge-case class performance, faster labeling speeds that streamline data pipelines, and improvements in model average precision (mAP) through effective data curation.

The platform enables robust model evaluation, allowing for the quick identification and correction of blind spots or data drift, ensuring models remain accurate amid evolving data landscapes. Furthermore, it facilitates the comparison of model performance and integrates human oversight in active learning workflows, accelerating the AI development lifecycle from initial data curation to final deployment.

Encord’s advanced label validation features and tools are meant to create balanced datasets, automatically surfacing label errors and validating labels to enhance ML model performance. Also, there is support for vector embeddings and AI-assisted quality metrics to help users easily identify and correct problematic data samples.

This focus on ensuring high-quality training data, combined with the ability to inspect model predictions compared to ground truth, allows teams to communicate errors effectively back to the labeling team. And as a result, we all get to benefit from better AI apps and services. Neat.