Machine Learning Infrastructure for the Data Privacy Age.
With data becoming more regulated and decentralized, the future is federated.
Customer Data Custody
Avoid the legal complexity of customer data ingestion. Deploy your models to your customers' data directly, no matter where it is.
Secure Collaboration
Work together with customers to craft safe data partitions to enable federated training for base model improvements.
Fully Automated
Programmatically deploy full ML pipelines across 1000s of customer deployments. From data to training to inference.
Data privacy concerns are making centralized ML architectures infeasible
Customers are more reluctant than ever to transmit confidential data to service providers for processing, and with good reason. Giving custody of data to third-parties comes with a host of complications.
Federated architecture makes it easy.
Avoid the complexity and liability of customer data ingestion by deploying your analytics directly on customer data, in their infrastructure.
- Local Data, Central Control
proxiML's federated infrastructure allows you to separate the data plane from the control plane. Manage customer inference activity from your cloud while ensuring data processing never leaves their premises.
- Separate Training and Inference Data
Federated training enables customers to contribute back to your base model development. They can choose which data is available for training versus inference.
- Any Infrastructure, Anywhere
Whether your customer's data is in cloud storage on the west coast or in a hospital data center in South Africa, they can easily provision a proxiML deployment to run your analytics.
EMBRACE DATA PRIVACY