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Enterprise-Scale Data Labeling & Automated Model Training with the Free Annotation Lab

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. It can be used for healthcare and fintech NLP.

About the speaker

Nabin Khada

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.

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