Show Notes

• (01:39) Aparna talked about her Bachelor’s degree in Electrical Engineering and Computer Science at UC Berkeley.
• (02:50) Aparna shared her undergraduate research experience at the Energy and Sustainable Technologies lab.
• (04:34) Aparna discussed valuable lessons learned from her industry internships at TubeMogul and compared the objective with that of a research environment.
• (08:26) Aparna then joined Uber as a software engineer on the Marketplace Forecasting team, where she led the development of Uber’s first model lifecycle management system for running ML model computations at scale to power Uber’s dynamic pricing algorithms.
• (12:40) Aparna talked about how she became interested in model monitoring while Uber’s model store.
• (17:29) Aparna discussed her decision to join the Ph.D. program in Computer Vision at Cornell University, specifically about bias in model, after spending 3 years at Uber.
• (23:40) Aparna shared the backstory behind co-founding MonitorML with her brother Eswar and going through the 2019 summer batch of Y-Combinator.
• (26:47) Aparna discussed the acquisition of MonitorML by Arize AI, where she’s currently the Chief Product Officer.
• (28:41) Aparna unpacked the key insights in her ongoing ML Observability blog series, which argues that model observability is the foundational platform that empowers teams to continually deliver and improve results from the lab to production.
• (33:17) Aparna shared her verdict for the ML tooling ecosystem in the upcoming years from her in-depth exploration of ML infrastructure tools covering data preparation, model building, model validation, and model serving.
• (37:01) Aparna briefly shared the challenges encountered to get the first cohort of customers for Arize.
• (39:23) Aparna went over valuable lessons to attract the right people who are excited about Arize’s mission.
• (41:04) Aparna shared her advice for founders who are in the process of finding the right investors for their companies.
• (42:24) Aparna reasoned how participating in The Amazing Race was similar to running a startup.
• (44:59) Closing segment.

Aparna’s Contact Info

• Twitter
• LinkedIn
• Medium
• Forbes Column
• Website
• Github
• Google Scholar

Arize’s Resources

• Website
• Medium
• LinkedIn
• Twitter

Mentioned Content

Blog Posts

• ML Infrastructure Tools for Data Preparation (May 2020)
• ML Infrastructure Tools for Model Building (May 2020)
• ML Infrastructure Tools for Production (Part 1) (May 2020)
• ML Infrastructure Tools for Production (Part 2) (Sep 2020)
• ML Infrastructure Tools — ML Observability (Feb 2021)
• The Model’s Shipped — What Could Possibly Go Wrong? (Feb 2021)

People

• Rediet Abebe (Assistant Professor of Computer Science at UC Berkeley and Junior Fellow at the Harvard Society of Fellows)
• Timnit Gebru (Founder of Black in AI, Ex-Research Scientist at Google)
• Serge Belongie (Professor of Computer Science at Cornell and Aparna’s past Ph.D. advisor)
• Solon Barocas (Principal Researcher at Microsoft Research and Adjunct Assistant Professor of Information Science at Cornell)
• Manish Raghavan (Ph.D. candidate in the Computer Science department at Cornell)
• Kate Crawford (Principal Researcher at Microsoft Research and Co-founder/Director of research at NYU’s AI Now Institute)

Book

• “The Hard Thing About The Hard Things” (by Ben Horowitz)

New Updates

Since the podcast was recorded, a lot has happened at Arize AI!

• Aparna has continued writing the ML observability series: The Playbook to Monitor Your Model’s Performance in Production (March 2021) and Beyond Monitoring: The Rise of Observability (May 2021).
• Arize has been recognized in Forbes’s AI 50 2021: Most Promising AI Companies.
• Aparna has also contributed to Forbes various articles: from the Chronicles of AI Ethics and Q&A with Ethics researchers, to a list of Women in AI to watch and emerging ML tooling categories.

About The Show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing [email protected].

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