Discover how leading healthcare and life science companies are accelerating NLP application development using data-centric AI workflows in this 25-minute presentation. Learn about Genentech and Memorial Sloan Kettering Cancer Research Center's success in reducing AI development time from months to days with Snorkel Flow. Explore real-world use cases, including entity extraction with 99.1% accuracy and demographic trend identification among patients. Gain insights into overcoming manual data labeling bottlenecks, implementing iterative training data development, and integrating data-centric AI workflows with existing ML stacks. Presented by Nazanin Makkinejad, a machine learning solutions engineer at Snorkel AI with extensive experience in deep learning and biomedical engineering.
How Top Healthcare Companies Boost AI Development with Data-Centric Approaches
Snorkel AI via YouTube
Overview
Syllabus
Example Al use cases in healthcare / life sciences
Data has become the blocker to realizing the promise of Al at scale
Training data development is iterative- not a one-time process
Pain: bottlenecked by manual data labeling
Snorkel Flow unlocks the complete data-centric Al development workflow
With Snorkel Flow, Genentech built an Al application to extract entities with 99.1% accuracy
With Snorkel Flow, they built an application to identify demographic trends among patients.
Procedure Extraction
Deep support for the data types and ML tasks to power diverse use cases
Snorkel Flow integrates easily with your existing ML stack
Taught by
Snorkel AI
