Show HN: Integrate.ai – Machine learning and analytics on hard-to-access data
Hey HN! This is Steve from integrate.ai (https://integrate.ai). Our platform unlocks 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. Given the increasingly distributed nature of data - across organizations, across borders, and across connected devices - centralizing the data necessary for machine learning and analytics often requires complex data-sharing agreements, costly pipelines, and supporting infrastructure.

The data governance challenges and cost implications of data centralization are blocking the world’s most important data-driven problems, particularly in healthcare, industrial IoT, and finance, where data custodians must enforce the highest privacy and security standards to ensure regulatory and contractual compliance and data is spread across multiple silos or edge environments. Here are a few examples where barriers to data access either slow down or completely block world-changing use cases:

(1) Rare disease diagnostics - Research hospitals don’t have enough rare disease data points to build accurate diagnostic models, and they cannot get access to more data from partner hospitals to improve their models

(2) Precision medicine - Health data consortiums working on improving medical care for diseases from cancer to heart disease can take years setting up data sharing agreements and implementing governance processes before research begins

(3) Drug safety monitoring - Government regulators can’t leverage data that is hard to standardize, such as laboratory, radiology, and pathology results, resulting in potential missed opportunities for detecting side effects or validating product safety

(4) Predictive maintenance - Manufacturers don’t have access to all of their data to train predictive maintenance models, because of challenges moving high volumes of data, sporadic data availability at the edge, and customer resistance to providing data access

(5) Financial fraud detection - Individual banks and credit card companies lack sufficient fraud data points to train highly accurate models, but face regulatory barriers and concerns about IP sensitivity that limits their ability to share data

integrate.ai leverages federated learning and differential privacy to enable machine learning and analytics on sensitive or hard-to-access data wherever it sits, without moving it. It allows data to stay distributed in its original protected environments, while unlocking its value with privacy-protecting machine learning and analytics. Model training and analytics are performed locally, and only end-results like model parameters are aggregated in a secure and confidential manner.

We empower product teams to extend machine learning and analytics capabilities in their products in flexible and reliable ways. integrate.ai is packaged as a developer tool, helping developers seamlessly integrate federated learning and federated analytics capabilities into almost any solution with an easy-to-use SDK and supporting cloud services 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.

You can start building your own federated models and analytics quickly, with our 14-day free trial: https://integrate.ai.

By adopting a federated approach and training models on data where it sits, without moving it, developers will be able to unlock solutions to the world’s most important problems without risking sensitive data. We would love to hear your thoughts on using hard-to-access data for machine learning and analytics, and your feedback on the product. Happy federating!