integrate.ai Announces Availability of New Platform for Collaborative Machine Learning and Analytics Across Sensitive Data

DNAstack utilizes the platform’s federated learning capabilities for privacy-preserving machine learning and analytics powering autism research

integrate.ai, a SaaS company helping developers solve the world’s most important problems without risking sensitive data, announces the availability of its privacy-preserving machine learning and analytics platform.

The platform leverages federated learning and differential privacy technologies to unlock a range of machine learning and analytics capabilities on data that would otherwise be difficult or impossible to access due to privacy, confidentiality, or technical hurdles. Traditional approaches to machine learning and analytics require centralization and aggregation of data sources, often necessitating data-sharing agreements and supporting infrastructure. This can present an insurmountable roadblock for the world’s most important data-driven problems, particularly in the healthcare, industrial, and finance sectors, where data custodians must enforce the highest privacy and security standards to ensure regulatory and contractual compliance. With integrate.ai’s solution, collaboration barriers can be broken as data does not need to move. It allows data to stay distributed in its original protected environments, while unlocking its value with privacy-protective machine learning and analytics. Operations such as model training and analytics are performed locally, and only end-results are aggregated in a secure and confidential manner.

“Autism is complex and research has shown the value of connecting massive datasets to drive critical insights. Genetic and health datasets are large, sensitive, and globally distributed, making it impossible to bring them all together in one place”

“When data can be securely accessed and collaborated upon, we unlock boundless opportunities for life-saving research and innovation. By allowing organizations to work in a federated way, our platform helps reduce cost structure, accelerate progress against product roadmaps and capture new revenue opportunities—all with more speed and flexibility than any other solution on the market,” said Steve Irvine, founder and CEO of integrate.ai. “Business and technology leaders alike increasingly recognize the global shift towards a more distributed paradigm. After serving at the forefront of this shift over the past five years, this platform will continue to grow into a product suite of easy-to-use tools for developers addressing humanity’s greatest challenges.”

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integrate.ai is packaged as a developer tool, enabling developers to seamlessly integrate these capabilities into almost any solution with an easy-to-use software development kit (SDK) and supporting cloud service for end-to-end management. Once integrated, end-users can collaborate across sensitive data sets while data custodians retain full control. Solutions incorporating integrate.ai can serve as both effective experimentation tools and production-ready services.

DNAstack, a company that offers software for scientists to more efficiently find, access, and analyze the world’s exponentially growing volumes of genomic and biomedical data, is using integrate.ai’s product platform to support federated learning in their work in autism. DNAstack leads the Autism Sharing Initiative, an international collaboration to create the largest federated network of autism data, empowering better genetic insights and accelerating precision healthcare approaches.

“Autism is complex and research has shown the value of connecting massive datasets to drive critical insights. Genetic and health datasets are large, sensitive, and globally distributed, making it impossible to bring them all together in one place,” said Marc Fiume, co-founder and CEO of DNAstack. “Federated learning will empower us to ask new questions about autism across global networks while preserving privacy of research participants.”

In the heavily regulated worlds of healthcare, financial services, and manufacturing, roadblocks to collaborating with sensitive data abound – from existing and proposed privacy regulations and intellectual property (IP) concerns to the high cost of centralizing massive datasets. Data science initiatives often fail or never start in the areas where their impact could be most life changing, such as early cancer diagnoses and detections of fraud, underscoring the considerable need for privacy-preserving data analytics solutions. Armed with experience serving enterprises across six industries and the construction of its own data network, which leveraged 20B interactions between businesses and people, integrate.ai enables safe access to sensitive data with developer tools for privacy-safe machine learning and analytics.

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