Scaling Algorithms with Machine Learning: A Google Engineer's Journey

Waleed, a former academic turned Google engineer, shares his experience working on Google Maps and the challenges of scaling algorithms to cover vast areas. He discusses how machine learning became a useful tool in solving these challenges and how he eventually ended up managing a computer vision team in Pittsburgh. Waleed also shares his personal journey of initially avoiding machine learning due to past exhaustion from his Ph.D. and later finding it to be a valuable asset in his work.

MLOps Coffee Sessions #154 with Waleed Kadous, ML Scalability Challenges, co-hosted by Abi Aryan.

// Abstract
Dr. Waleed Kadous, Head of Engineering at Anyscale, discusses the challenges of scalability in machine learning and his company's efforts to solve them. The discussion covers the need for large-scale computing power, the importance of attention-based models, and the tension between big and small data.

// Bio
Dr. Waleed Kadous leads engineering at Anyscale, the company behind the open-source project Ray, the popular scalable AI platform. Prior to Anyscale, Waleed worked at Uber, where he led overall system architecture, evangelized machine learning, and led the Location and Maps teams. He previously worked at Google, where he founded the Android Location and Sensing team, responsible for the "blue dot" as well as ML algorithms underlying products like Google Fit.

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