Unleashing Sensitive Datasets with Distributed Data Science
Offered By: MLOps.community via YouTube
Course Description
Overview
Syllabus
[] Musical introduction to Blaise Thomson
[] From Intractable to Interactable: Unleashing Sensitive Datasets with Distributed Data Science
[] Outline
[] Introduction to Distributed Data Science
[] The context
[] What is going wrong?
[] Distributed Data Science
[] Sending Algorithms to Data
[] How Distributed Data Science solves the problem
[] Usage-based access control
[] Privacy protections
[] Dataset-level privacy-enhancing technologies
[] Collaboration-level privacy-enhancing technologies
[] Confidential Collaboration: Secure Aggregation and Federated Learning
[] Secure Aggregation
[] Federated Learning
[] An example experimental setup Retinal OCT Kaggle
[] An example: Error rates
[] Confidential Collaboration: Private Set Intersection
[] Private Set Intersection - Simple hashing
[] Private Set Intersection - more complex hashing
[] Other use cases
[] Configurations
[] Internal use within the company or group
[] Embedded: Single lead, leveraging partners' data
[] Data Resellers
[] Alliance - Consortium of data providers and data scientists
[] Summary
[] Sign up for the Bitfount Open Beta Launch at https://www.bitfount.com/!
[] Data Errors Obfuscation
[] Histograms with SQL queries on structured or unstructured data
[] Implicit bias detection or model failures
[] Theory vs Application
[] Skipping details approach
[] OpenMined PySyft
[] MLOops!
[] Siri products MLOops!
[] Wrap up
Taught by
MLOps.community
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