Your artificial intelligence (AI) and machine learning (ML) models don't deliver the right insights without high quality data.

While your engineers are tasked to build training models to test the efficacy and accuracy of data and then move on to build sophisticated large-scale ML models, they often spend more time preparing data.

Here are 2 reasons why your ML engineering team spends more time on data labeling.

Reason 1: Your ML engineer often conducts repeated tests to determine which label data is more suitable for the training algorithm. And conducting tests takes a lot of time.

Reason 2: Training a model needs tens of thousands or even millions of training data, which takes a lot of time. For example, an in-house team composed of 10 labelers and 3 QA inspectors can complete around 10,000 automatic driving lane image labeling in 8 days.

How can you unleash your engineering team from labeling data and let them do their job?

That's why we built Traindata Inc., to help you prepare your data through labeling, annotation, structuring, and cleaning of text, audio, and visual data at affordable costs.

Talk to us about your AL/ML data labeling challenges today, or visit www.traindata.us to learn more.