The company says about 150,000 users are currently relying on Label Studio, with more than 95 million annotations created so far. cd22b01a5c099a2f1b07940b5d4500d099aec1df-800x501_Z5WDf3 “When we were originally thinking about building the data labeling solution, we did a lot of data scientist interviews,” Heartex co-founder and CEO Michael Malyuk told me. “And what we figured out is that many of them say they don’t have enough flexibility with existing tools. There is a tool for images, but you can only put a single image on the screen. There is a tool for our audio, but it’s very hard-coded in terms of the use case. And we thought that for the Label Studio, when the data scientists would have a question mark inside their heads ‘does it support my use case?’ The answer always has to be yes — it always has to support your use case.” And, of course, to label data, all you need to be is a subject matter expert, not a software engineer. The idea behind Label Studio is to enable virtually anyone to label your data. “We think that every AI company is going to transform into a data labeling company or a dataset development company,” Malyuk said. Essentially, he said, Heartex wants to make Label Studio the de facto IDE for dataset development. Looking ahead, he noted that the company plans to invest heavily in its user community — and it plans to host its first Label Studio user conference next year. Heartex’s Label Studio makes labeling audio for ML easier by Frederic Lardinois originally published on TechCrunch
