Integrate AI – leading the movement from data to signals to eliminate data that hides what’s relevant and takes massive effort to prepare.
Integrate AI – leading the movement from data to signals to eliminate data that hides what’s relevant and takes massive effort to prepare.
Integrate AI – leading the movement from data to signals to eliminate data that hides what’s relevant and takes massive effort to prepare.

Federated Learning. Simplified.

PowerFlow is the end-to-end federated learning platform that makes it easy for data scientists to create performant models using decentralized data.

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Decentralized data is hard to use for machine learning.

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Compliance with privacy and data residency regulations requires data to remain within its jurisdiction or organization.

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Contractual limitations prevent companies from moving data or utilizing it outside of pre-defined use cases.

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Physical data silos within an organization require complex technical solutions to centralize.

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Data sharing agreements are difficult to write and challenging, time-consuming, and costly to execute.

PowerFlow makes it easy to train models with decentralized data and capture new machine learning opportunities.

Managed infrastructure

Infrastructure and simple management tools reduce implementation costs and timelines.

Built-in privacy

Differential privacy settings equip you with tools to understand and protect data and model privacy.

Model performance tools

Train performant models even with zero visibility into raw data.

Pre-configured observability

Model training reports help you understand which data sets add value to your model.

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What is Federated Learning?

Unlike traditional machine learning techniques that require data to be centralized for training, federated learning is a method for training models on decentralized datasets. Portions of a machine learning model are trained where the data is located (e.g., these could be private datasets from two or more companies) and model parameters are shared among participating siloed datasets to produce an improved model. No data moves within the system, which means that organizations can collaborate without compromising privacy or sensitive IP while avoiding the pain and expense of transferring data through traditional means.

Request a demo to see how easy federated learning can be with PowerFlow.

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