Abstract: 

In this session, we will explore the requirements and practical workflows for enterprise-grade machine learning (ML) at scale. Through the lens of Cloudera Machine Learning we will unpack common challenges and best practices associated with training, deploying, and maintaining ML models in production; and further understand how open source technology and standards enable holistic end-to-end ML workflows from data management to deploying into production and beyond.

Bio: 

Santiago leads strategy and product marketing for production machine learning and ML engineering at Cloudera. He has over 10 years of experience as a design technologist, researcher, and building data science products for global enterprises. Additionally, Santiago holds a Master of Science degree in Design and Urban Ecology from Parsons School for Design, is a fellow at the Urban Design Forum, and a board member for the Global Permaculture Institute where he continues to research the intersect between technology, data, and ecological systems.