Extracting data from unstructured documents is a common requirement - from finance and insurance to pharma and healthcare. Recent advances in deep learning offer impressive results on this task when models are trained on large enough datasets.

However, getting high-quality data involves a lot of manual effort. An annotation project is defined, annotation guidelines are specified, documents are imported, tasks are distributed among domain experts, a manager tracks the team's performance, inter-annotator agreement is reached, and the resulting annotations are exported into a standard format. At enterprise-scale, complexity grows due to the volume of projects, tasks, and users.

John Snow Labs' Annotation Lab is a free annotation tool that has already been deployed and used by large-scale enterprises for three years. This webinar presents how you can exploit the tool's capabilities to easily manage any annotation project - from small team to enterprise-wide. It also shows how models can be trained automatically, without writing a single line of code, and how any pre-trained model can be used to pre-annotate documents to speed up projects by 5x - since domain experts don't start annotating from scratch but correct and improve the models, as part of a no-code human-in-the-loop AI workflow.

Nabin Khadka, Lead Developer - Annotation Lab, John Snow Labs

Nabin Khada leads the team building the Annotation Lab at John Snow Labs. He has 7 years of experience as a software engineer, covering a broad range of technologies from web & mobile apps to distributed systems and large-scale machine learning.