Compared to DevOps or data engineering, MLOps is still relatively young as a discipline – and best practices are often learned on the fly. How can teams go from 10 to 100 or 1,000 models? And what are the best ways to ensure buy-in for a new ML platform or perform business impact analysis? In this panel of MLOps leaders who have built ML practices from the ground up to achieve enterprise scale, we will dive into best practices and blunt takeaways for teams ramping up.
Speakers
Chintan Turakhia
Director of Engineering, Coinbase
Korri Jones
Sr. Lead Machine Learning Engineer, Chick-fil-A
I believe in asking hard questions and embracing the unknown. From a technical standpoint, while complexity is something necessary, I believe in seeking simplicity and driving adoption. Finally, I am passionate about education at all levels, and the impact that investing my time, talents, and treasure into it over the long run.
Wendy Foster
Director of Engineering Data Science, Shopify
Wendy Foster is a Director of Engineering and Data Science at Shopify, where she leads Engineering and Data for the Insights and Commerce Algorithms groups, building merchant facing analytic tools and key data and machine learning models to improve product understanding across Shopify's merchant base. She has been a passionate sponsor for representation in technology spaces, and advocate for user-centered AI practices over her 10+ year career in the field.
Jason Lopatecki
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!
