Unleashing Sensitive Datasets with Distributed Data Science
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the world of distributed data science and its potential to unlock sensitive datasets in this 39-minute conference talk from the MLOps Community Meetup. Discover how sending algorithms to data can extract value while maintaining strict privacy and security guarantees. Learn about federated machine learning, secure aggregation, and private set intersection techniques. Gain insights into various configurations for implementing distributed data science, from internal company use to data reseller models. Understand the challenges of working with sensitive data and how distributed approaches can improve model quality. Dive into real-world examples, including a retinal OCT experiment, and explore the balance between theory and application in this emerging field. Get introduced to tools like Bitfount's platform and OpenMined PySyft, and hear about potential pitfalls and "MLOops" moments in distributed data science projects.