Scaleout Edge manages the full AI model lifecycle at the edge: federated training, versioned deployment, resilient operations, and fleet-wide observability, across fleets where data cannot move.
Standard ML assumes centralized data, cloud training, and static models. At the edge, none of those assumptions hold. Four fundamental constraints make traditional approaches break down.
Privacy law and classification boundaries make centralization non-compliant.
Contested environments operate offline or with denied connectivity.
Local patterns change faster than centralized retraining cycles.
No central record of model versions, lineage, or accountability.
Scaleout Edge is built around four principles, each a direct response to edge constraints.
Edge nodes train on local data as conditions change. No centralized retraining cycles.
Raw data stays on device. Only encrypted weight adjustments travel to aggregation.
Nodes train independently. Updates sync when connectivity allows.
Full provenance across the fleet. Model versions recorded with lineage.
Scaleout Edge doesn't replace your ML tooling. It sits between your development environment and the edge fleet, handling the distributed layer your existing tools don't.
Build and test models in SageMaker, MLflow, or Kubeflow. Automate sessions through the REST API and Python APIClient from your CI/CD pipelines. Export fleet telemetry via OpenTelemetry to Prometheus, Grafana, or Datadog.
Distributed data that cannot move and AI that must keep improving.
Drones, unmanned systems, and forward-deployed sensors operating in contested or bandwidth-denied environments.
Oil & gas platforms, mines, and energy grids where data is contractually bound to physical location.
Thousands of vehicles generating terabytes of telemetry daily. Too much to centralize, too valuable to ignore.
High-value datasets in government, healthcare, and finance siloed by privacy law and classification.
Pre-built, federated-ready modules for the most common edge AI domains. Each includes model architectures, training configurations, and reference workflows. Designed to compress months of integration into days.
Federated-ready model architectures for detection, classification, and segmentation. Supports continuous improvement of vision models across distributed fleets via federated fine-tuning. Each node learns from its local environment without sharing raw imagery.
Modular toolkit for unmanned vehicles. On-drone inference with offline caching and sync, autonomous flight ops for recon and engagement, and swarm coordination with ground station integration.
Federated fine-tuning of speech and language models on local private data. Reference workflows for Whisper, Wave2vec, and transformer-based LLMs, with edge-optimized deployment targeting hardware like NVIDIA Jetson.
Test whether your federated models leak private data. Privacy auditing covers model inversion, membership inference, and gradient inversion. Adversarial simulations include data poisoning, backdoor attacks, and label flipping.
Scaleout Edge is deployed as a single-tenant instance under your control. We scope the deployment with your team, from initial pilot to production fleet.
Single-tenant deployment on cloud or on-prem infrastructure. Includes access to all capability modules, dedicated onboarding, and engineering support.
Free licenses for accredited institutions and students for non-commercial research.
How pricing works: Pricing adjusts based on deployment scope (active environments and aggregation points), node capacity (concurrent edge devices), and support tier. A natural starting point is a 60-day Stage 1 engagement to establish the lab workbench, demonstrate the full workflow, and jointly define the path to field validation.