Abstract:
More data means more robust and effective machine learning models. Traditional machine learning techniques rely on centralization of data therefore data silos are a blocker to achieving the desired performance. Federated Learning, is a well studied theoretical methodology that breaks the barrier of data silos by bringing the training of machine learning models to the datasets. In the recent years more efforts have been made to pave the way of moving this research concept into product. In this session we would like to help users become familiar with the concept and get an understanding of benefits and challenges in a hands on environment.

What You’ll Learn:
The applied challenges of cutting edge techniques in dealing with distributed data

Roshan is a startup addict (she has been among the first 10 employees at 3 successful startups) who is passionate about delivering real value to real people through machine learning products. A proud Waterloo graduate, she has spent 10+ years devoted to the art and science of researching, developing and productionizing machine learning products. She has delivered $100s of millions of value to companies using AI in a wide variety of use cases from wearables to warehouse management to marketing. Ro is a proud mom and art enthusiast (she loves to sketch and hang out at indie art exhibitions).

Nasron Cheong leads engineering at integrate.ai, and helps shape engineering practices needed for integrate.ai to develop privacy safe ML products. His experience includes large scale mobile analytics on big data platforms, as well as MLOps experience with various commercial and open source offerings