Observability is arguably the hottest area of machine learning today. The massive investments companies have put toward digital transformation and building data-centric businesses in the last decade are manifesting as machine learning models in production – yet there’s a gap in the infrastructure required to maintain and improve these models once they are deployed into the real world. In this session, we will explore the state of the ML infrastructure ecosystem, key considerations when building an ML observability practice that can deliver tangible ROI across your organization, and see what’s on the horizon of Arize’s product roadmap.
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.
Co-founder and CEO, Arize AI
Jason Lopatecki is co-founder and CEO of Arize AI, a machine learning observability company. He is a garage-to-IPO executive with an extensive background in building marketing-leading products and businesses that heavily leverage analytics. Prior to Arize, Jason was co-founder and chief innovation officer at TubeMogul where he scaled the business into a public company and eventual acquisition by Adobe. Jason has hands-on knowledge of big data architectures, programmatic advertising systems, distributed systems, and machine learning and data processing architectures. In his free time, Jason tinkers with personal machine learning projects as a hobby, with a special interest in unsupervised learning and deep neural networks. He holds an electrical engineering and computer science degree from UC Berkeley - Go Bears!