Modern AI application development is shifting from a model-centric practice to a data-centric approach. Rather than focusing solely on models trained over static datasets, practitioners are thinking more holistically about their pipelines, with a renewed emphasis on the training data.
During this webinar, you will gain an in-depth understanding of
- The principles and strategies of Data-centric AI
- How to build high-quality applications using the Snorkel framework
- The techniques for iteration, adaptation, and collaboration
- How to empower domain experts to accelerate AI application development
Machine Learning Engineer
About the presenters
is a Machine Learning Engineer at Snorkel AI. She graduated from Columbia University with a Master's degree in Computer Science specializing in Machine Learning. She has previously worked as a Software Engineer at Microsoft Azure and an intern at Amazon Robotics. She is a vocal supporter of women in tech and served as the Director for Women Who Code Delhi.
is an engineering leader at Snorkel AI, building the core programmatic labeling and iterative model development experience. Previously, she managed teams building internal tools for the AI/ML organization at Apple and was one of the founding engineers on Google Photos. She received her Ph.D. in Software Engineering from UC San Diego. She believes that the true potential for machine learning can be unlocked by collaborating on the data.