Secure Collaboration on AI: AWS Clean Rooms ML Empowers Companies
Amazon has launched a privacy-preserving service that enables AWS customers to deploy AI models trained on proprietary data without sharing it with outside partners. The service, called Clean Room ML, allows customers to train private lookalike models using collected data while keeping full control over the model. It also offers the ability to tune the model’s output based on specific business needs. Amazon plans to add specific applications, such as healthcare, in the near future. The service differentiates itself by fully managing privacy and obfuscating uploaded custom data.
Table of Contents: Secure Collaboration on AI: AWS Clean Rooms ML Empowers Companies
- Amazon introduces PrivacyPreserve service for AI model training with clean room collaborations
- Clean Room ML enables custom deployment of privacy-preserving AI models
- AWS Clean Room ML allows companies to build, train, and deploy AI models with privacy in mind
- Swami Sivasubramanian discusses the key features of Clean Room ML at AWS re:Invent
- Clean Room ML empowers customization by generating expanded sets of similar records for partner collaborations
- Amazon unveils Clean Room, a privacy-focused service for managing and analyzing custom data
Amazon introduces PrivacyPreserve service for AI model training with clean room collaborations
Amazon introduces a new service called PrivacyPreserve, a clean room collaboration solution that enables enterprises to train artificial intelligence (AI) models with sensitive data, without sharing it directly with third parties. PrivacyPreserve allows companies to securely collaborate with each other or with Amazon Web Services (AWS) to build, train, and deploy AI models without compromising the privacy of their data. This service eliminates the need for customers to share their proprietary data outside of their organization, ensuring the security and confidentiality of sensitive information. With PrivacyPreserve, companies can leverage AI to uncover valuable insights and make informed decisions without exposing their data to unauthorized access. For instance, an airline company may want to identify loyal customers who frequently book online and offer them special promotions. PrivacyPreserve would enable the airline to collaborate with AWS to build a lookalike AI model using the airline’s customer data, without sharing any personally identifiable information with AWS.
Clean Room ML enables custom deployment of privacy-preserving AI models
Amazon has launched a preview of its Privacy Preserving Service, which allows custom deployment of privacy-preserving AI models. With this service, customers can train and deploy AI models without sharing their proprietary data outside of their organization or with any third parties. This is achieved through the use of Clean Room ML, an offshoot of Amazon’s existing Clean Room product and service. Clean Room ML eliminates the need for customers to share or move their data by generating synthetic data that preserves the statistical properties of the original data. This enables customers to build, train, and deploy AI models with private data without compromising on accuracy or performance. For example, an airline might use Clean Room ML to train a model to identify loyal customers who book online and use the service frequently, and then use this model to offer promotions and discounts to similar users. Clean Room ML also offers controls to tune the model output based on specific business needs. In the near future, Amazon plans to add a set of specific applications in healthcare in relation to this announcement.
AWS Clean Room ML allows companies to build, train, and deploy AI models with privacy in mind
Amazon has unveiled Clean Room ML, a privacy-preserving service that allows companies to build, train, and deploy artificial intelligence (AI) models without sharing proprietary data outside of their organization. It is an offshoot of Amazon’s existing Clean Room product and service, which removes the need for Amazon customers to share their proprietary data with partners in order to build, train, and deploy their AI models. Swami Sivasubramanian, Vice President of Data and Machine Learning Services at Amazon Web Services (AWS), explained during his keynote address at the AWS Reinvent conference that Clean Room ML enables customers to take a small sample of their customer records, generate an expanded set of similar records, and then train lookalike AI models across the collected data. For instance, an airline company could leverage Clean Room ML to identify signals of loyal customers who frequently book online and then use this information to design targeted promotions for similar users. Clean Room ML provides granular control and the ability to tune model outputs based on specific business needs.
Swami Sivasubramanian discusses the key features of Clean Room ML at AWS re:Invent
At AWS re:Invent, Swami Sivasubramanian, VP of Data and Machine Learning Services at Amazon Web Services (AWS), introduced Clean Room ML, a preview of a new privacy-preserving service. Clean Room ML allows customers to train and deploy AI models with their own proprietary data without sharing it with AWS or any other party. This is achieved through a variety of techniques, including data obfuscation, aggregation, and differential privacy. Clean Room ML also offers control over the model output, allowing customers to tune the model to meet their specific business needs. Sivasubramanian noted that AWS plans to add a set of specific applications, such as healthcare, in the near future.
Clean Room ML empowers customization by generating expanded sets of similar records for partner collaborations
Amazon’s Clean Room ML empowers customization by generating expanded sets of similar records for partner collaborations. It eliminates the need for AWS customers to share proprietary data outside their organization to build, train, and deploy AI models with partners. Clean Room ML trains private lookalike models across collected data, enabling partners to reach similar users. For instance, an airline might use Clean Room ML to analyze signals of loyal customers who book services online and offer promotions to new, similar users. Clean Room ML provides control to tune model outputs based on specific business needs. In the near future, AWS plans to add a set of specific applications in healthcare, in relation to the recently announced Amazon HealthLake. Unveiling Clean Room as a fully managed service differentiates it from existing Clean Room products and services. Clean Room’s privacy-preserving service allows customers to upload their data, generate aggregated insights, and combine these insights with things like advertising campaign investment decisions, without exposing proprietary data.
Amazon unveils Clean Room, a privacy-focused service for managing and analyzing custom data
In a groundbreaking move that underscores its commitment to privacy preservation, Amazon has introduced Clean Room, a novel service that provides a secure and controlled environment for managing and analyzing custom data. This state-of-the-art service empowers organizations, or ‘customs’, to deploy custom machine learning models, train lookalike models, and conduct one-off analyses on their own data without the need to share their proprietary data outside of the partnership. With Clean Room, Amazon seeks to revolutionize how companies collaborate and leverage artificial intelligence (AI) models while ensuring complete control and privacy of their sensitive information.