Current rates of data growth means that we will rapidly run out people to label data.
Targeted automation with micro-models
Rather than building large monolithic models, decompose your labeling task into much smaller parts and automate each one with micro-models.
The problem with purely manual approaches
Current methods of annotation require workforces of hundreds of thousands of people. This is fundamentally constrained, produces variable quality, and does not work for use cases where data privacy is important.
Humans are error-prone and oftentimes disagree on what constitutes ground truth.
Shipping data back and forth between large annotation teams is slow and costly.
The benefits of automation
Encord's system removes human bottlenecks with novel automation techniques allowing you to quickly create large high-calibre training datasets.
A fresh perspective on computer vision
Rather than training large monolithic models, Encord breaks down annotation tasks into atomic units, allowing you to train, run, and ensemble targeted micro-models.
Download & deploy
Download micro-models for integration with downstream applications with the click of a button. Deployable in secure environments and on edge.
Active learning pipelines
Integrate micro-models into existing data pipelines with our APIs and SDK. Combine your production model and micro-models with ease.
Automating data annotation for computer vision
Use automation to save on human supervision and enhance quality.
Encord has developed a wide range of automation features to annotate datasets to the highest quality standards to reduce the bottleneck of manual labour in the annotation process. These features include proprietary sampling, tracking, interpolation, auto-segmentation algorithms, and several intelligent heuristics. However, the core of our technology is a novel approach we call micro-models.
We believed there must be a better way to make AI practical from first starting the company. We have devised a unique and effective methodology for automating and streamlining the tasks related to preparing and managing quality training data.
In contrast to traditional machine learning models that require large quantities of data and are fit for robustness and generalisability, our micro-models are tightly scoped and over-fit to narrow tasks and data distributions.
Our technology allows you to train micro-models in only a few minutes, starting with just a handful of labels, and then ensemble many micro-models together to cover your complete set of labeling tasks. Our platform allows you to assemble micro-models to cover arbitrarily complex annotation tasks.