AutoLabel

Automate 80% - 90% of manual data labeling effort

autoLabel is a human-in-the-loop, active learning system which sorts data by uncertainty, and human labelers start by labeling a small, automatically and carefully selected portion of the data. The autoLabel system trains a model on the labeled data and uses it to sort the remaining unlabeled data in order of confidence. Human labelers label the most informative images and autoLabel improves its model in several iterations. After 5% - 16% of the data is labeled manually, the confidence is typically so high that no further manual labeling needs to happen, and the remaining dataset can be labeled automatically. These automatically labeled images can now be checked by human domain experts with over 80% less effort than creating the labels themselves.

As the complexity and scale of datasets increase, the processes surrounding labeling datasets also need to evolve. You need a comprehensive toolset to accelerate labeling projects, reduce cost, and maintain high quality output. Structure, automate and qualify your labeling workflows by using the RedBrick AI platform. By building a completely custom labeling workflow, your team can easily carry out autoLabel active learning iterations.

Gain and maintain visibility into the productivity of your workforce, and track how autoLabel automates data labeling over time. Your labeling tasks will get automatically routed in the Active Learning workflow, and assigned to the appropriate stakeholders -- simplify the project management and focus on your science.

The rapid pace of AI innovation makes designing and training accurate AI models challenging. With autoLabel, you can eliminate guesswork and get started faster by automating the initial tasks of labeling data. This will get you a fully, accurately labeled dataset in the most efficient way possible. After that, you can train and fine-tune your model using Brightics AI Accelerator and later run it at inference on unlabeled data.

Compared to manually labeling all of the dataset, autoLabel reduces the time it takes to start labeling 66% and the Total-Cost-of-Ownership (TCO) up to 54% depending on the use case.

Because Samsung SDS’s autoLabel solution pre-processes the dataset in order of confusion before the first manual labeling iteration, it is able to exploit the most informative data to reduce active learning iterations and cloud infrastructure costs 33% over competing cloud offerings.