End-to-End No-Code Development of AI Models for Text and Images
AI models and pipelines for text and image processing are currently used in intelligent applications on all verticals, from Healthcare to Finance and Security. Up until now, they have been accessible to a handful of people: hands-on experts in deep learning and transfer learning. It is time to make them accessible to everyone!
To this purpose, we have created a Free, End-to-End, No-Code platform that can be used by domain experts – nurses, doctors, lawyers, accountants, investors, etc. – to extract meaningful facts automatically from documents or images. This can be done by using state-of-the-art pre-trained models or by customizing those models to better handle their use cases.
John Snow Labs’ Annotation Lab supports the end-to-end process from starting an annotation project to the deployment of a trained model, all without writing a line of code. Based on an auto-scaling architecture powered by Kubernetes, it can scale to many teams and projects. Enterprise-grade security is provided for free including support for air-gap environments, zero data sharing, role-based access, full audit trails, MFA, and identity provider integrations. It allows powerful experiments for model training and finetuning, model testing, and model deployment as API endpoints.
Head of Product at John Snow Labs
Dia Trambitas is a computer scientist with a rich background in Natural Language Processing. She leads the development of the Annotation Lab, currently the best-in-class tool for text and image annotation for healthcare.
Dia holds a Ph.D. in Computer Science focused on Semantic Web and ontology-based reasoning. She has a vivid interest in text processing and data extraction from unstructured documents, a subject she has been working on for the last decade. She has broad experience delivering information extraction and data science projects across Finance, Investment Banking, Life Science, and Healthcare.