Production data labeling workflows
It’s one thing to gather some labels for your data. It’s another thing to integrate data labeling into your workflows and infrastructure in a scalable, secure, and useful way. Mark from Xelex joins us to talk through some of what he has learned after helping companies scale their data annotation efforts. We get into workflow management, labeling instructions, team dynamics, and quality assessment. This is a super practical episode!
Matched from the episode's transcript 👇
Mark Christensen: We didn’t come out of a data science discipline, actually; we came out of healthcare. And we’ve spent the last 17 years managing healthcare data at scale, specifically in the area of dictation and transcription. So an entirely different field. But the thing we had in common was managing large amounts of data at scale. And in healthcare, what we would do is we would record audio, and for 17 years we recorded audio from healthcare providers, and then moved that audio through an enrichment workflow, which just essentially was transcription. So we’d have skilled medical transcriptionists in the States and around the world who would take the audio and transcribe it into the completed healthcare note.
And a few years ago, we met with a friend of ours, who was an owner of an NLP company, and he really liked our platform; we happened to be working with him on a speech recognition project… And we really have a need for this, and managing trading data for NLP workflows. And so that launched a discovery process that took about two years, and we investigated the use case and determined that there was a really neat fit. And so we modified the application for the next two years, and then launched our training data services workflow called Xelex.ai. So that’s how we got into it.