Zhichun
Li
GM of Rapid at Scale AI
"Unlocking ML Experimentation by Providing the “Accessible Luxury” of Data"
"Unlocking ML Experimentation by Providing the “Accessible Luxury” of Data"
08 - 2:50 PM EDT/New York (See in local time)
Add to Calendar
04//2024 2:50 PM
04//2024 03:00 PM
America/New_York
#WTGC2024
"Unlocking ML Experimentation by Providing the “Accessible Luxury” of Data"
#WTGC2024
"Unlocking ML Experimentation by Providing the “Accessible Luxury” of Data"
https://app.events.ringcentral.com/events/women-in-tech-global-conference-2024/reception
https://www.womentech.net/vfairs
"Unlocking ML Experimentation by Providing the “Accessible Luxury” of Data"
"Unlocking ML Experimentation by Providing the “Accessible Luxury” of Data"
Don’t miss out and join visionaries, innovators, and thought leaders from all over the world at the Women in Tech Global Conference.
Vote by Sharing
Unite 100 000 Women in Tech to Drive Change with Purpose and Impact.
Do you want to see this session? Help increase the sharing count and the session visibility. Sessions with +10 votes will be available to career ticket holders.
Please note that it might take some time until your share & vote is reflected.
Session: Unlocking ML Experimentation by Providing the “Accessible Luxury” of Data
For many machine learning (ML) teams at scrappy startups and research organizations, acquiring high-quality labeled data is a key barrier to ML. These teams are often locked out of this experimentation due to a lack of resources, financing or headcount to build, monitor and evolve production-quality data pipelines.In this talk, Zhichun Li, GM of Rapid at Scale AI will shine a light on the barriers to acquiring production-level data and how expanding access to high-quality data will help companies of all sizes bootstrap their ML efforts and stay agile. Using Scale Rapid as an example, she will delve into the technical and operational challenges inherent to acquiring high-quality data including getting fast and real-time feedback on labeling instructions, edge case detection and scaling to production.
Key Takeaways
- Democratizing data for startups and companies of all sizes
- How to bootstrap ML efforts by leveraging access to production-quality labels
- The importance of monitoring data quality to produce safer and better results
Bio
Zhichun Li is the GM of Rapid at Scale AI. She built the team from scratch with a focus on providing the fastest way to production-level quality labels within a day, with no data minimums. As an early employee of the company, she built up the infrastructure for Scale’s supply ops system and scaled up Scale’s 3D Sensor Fusion product. Before Scale, Zhi worked at Lightspeed China Partners, Facebook, Microsoft and Airbnb with roles in investment and software engineering. She was the youngest ever admit to the Yale MBA program, and studied computer science at CMU.