How do you justify building an ML platform internally? What are the key components that matter to your team? And why is ML infrastructure necessarily distinct from software infrastructure? In this lively panel of ML executives and pioneers, we will debate and explore these and other questions and share best practices, war stories and more.
Director of Engineering, Uber
Machine Learning Platform Product Lead, Spotify
Josh leads ML Platform at Spotify, growing an organization of product, design and engineering hyperfocused on increasing the productivity of ML practitioners. During his 8 years at Spotify, he has built data products as an engineer and product leader. He holds a MS in Computer Science from NYU and a BS in Philosophy/CS from the University of Pittsburgh. He's spent the pandemic on the Brooklyn waterfront with his partner, cat, and 9-month old daughter "Kiki".
Co-Founder and CPO, Arize AI
Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in machine learning (ML) observability. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30 in the Enterprise Technology category. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML infrastructure platforms, including Michealangelo. She has a bachelor’s from Berkeley’s Electrical Engineering and Computer Science program, where she published research with Berkeley’s AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.