Federated Machine Learning: A Scalable, Privacy-Preserving Approach Ready for Production

Explore the potential of federated machine learning in production environments through this insightful conference talk. Delve into the core concepts and essential features required for developing enterprise-grade federated learning platforms, with a focus on security, data privacy, scalability, fault tolerance, and performance in geographically distributed settings. Gain valuable insights from two active use cases demonstrating the application of regulated datasets across distributed locations. Learn from Salman Toor, an expert in federated machine learning, scientific data management, and distributed computing infrastructure, as he shares his expertise on this thriving area of research. Discover how federated machine learning creates new possibilities for privacy-preserving data analysis and its potential impact on ML engineers working in production environments.