Create a complex Machine Learning model for a domain of knowledge without writing code using Watson Knowledge Studio.


In this video:

About this webcast

One of the key benefits of building a machine learning annotator is the ability to train Watson in a complex domain such as medicine. Learn the methodology, standard practices, and guiderails on how to go about building an effective ML model. Steps include data understanding, type system building, pre-annotation, and deployment to WDS. After this session, you will have an acute understanding of what goes behind building an effective ML model.

Additional resources for this webcast

Visit the WKS Medical Domain site for the presentations and materials demoed in this webcast.

About the Speakers

Avinash Asthana is the Global Watson Practice Lead for Watson Knowledge Studio responsible for creating standard practices and assets required for WKS implementation. He has worked with WKS since its beta form in 2015 and has been involved in numerous first of a kind projects involving WKS across geos since then.

Jocelyn Kong has been with IBM for about a year and a half, working in the Astor Place, NYC office. She is a developer on the Watson Implementations teamand has primarily worked on client projects implementing cognitive systems and POCs based on Watson services. She is currently working on the Watson Business Solutions team which aims to build easily deliverable Watson-based applications for clients.


Watson Knowledge Studio

Watson Discovery Service

Natural Language Understanding

Use Cases and Case Studies


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