Fueling innovative software
July 15-18, 2019
Portland, OR
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Animesh Singh

Animesh Singh
Senior Technical Staff Member and Program Director, IBM

Animesh Singh is a senior technical staff member (STSM) and program director for the IBM Watson and Cloud Platform, where he leads machine learning and deep learning initiatives on IBM Cloud and works with communities and customers to design and implement deep learning, machine learning, and cloud computing frameworks. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. Animesh has worked on cutting-edge projects for IBM enterprise customers in the telco, banking, and healthcare industries, particularly focusing on cloud and virtualization technologies, and led the design and development first IBM public cloud offering.

Sessions

1:30pm–5:00pm Tuesday, July 16, 2019
Building a secure and transparent ML pipeline using open source technologies
Incorporating Artificial Intelligence
 Location: C123-124
Secondary topics:  AI Enhanced
Animesh Singh (IBM), SVETLANA Levitan (IBM), Tommy Li (IBM)
Average rating: *....
(1.80, 5 ratings)
Given the growing demand for fairness, accountability, and transparency from machine learning (ML) systems, Animesh Singh, Svetlana Levitan, and Tommy Li demonstrate how to build an ML pipeline that's open, secure, and fair and that fully integrates into the AI lifecycle, using open source tools like AI Fairness 360 (AIF360) and Adversarial Robustness Toolbox (ART), among others. Read more.
1:45pm–2:25pm Wednesday, July 17, 2019
End-to-end transparent, secure, and trusted machine learning pipelines using open source technologies (sponsored by IBM)
Sponsored
 Location: F151
Animesh Singh (IBM)
Average rating: ****.
(4.00, 4 ratings)
There's a growing demand for fairness, accountability, and transparency from machine learning (ML) systems. We need a pipeline that's open, transparent, secure, and fair and that fully integrates into the AI lifecycle. Animesh Singh examines how to build such a pipeline while leveraging open source projects. Read more.