Scaleout Edge Overview

Scaleout Edge is a sovereign edge AI infrastructure platform designed to orchestrate machine learning from edge to cloud. It acts as a secure control plane for governed Edge AI, managing the full lifecycle of models across decentralized fleets—from on-premise data centers to constrained edge devices—without requiring raw data to ever leave its source.

While traditional machine learning pipelines are centralized, requiring data to be moved to a centralized storage, Scaleout Edge inverts this workflow. It brings the model to the data.

Scaleout Edge is designed as mission-critical infrastructure for distributed environments. It is not a general-purpose MLOps platform, but a specialized orchestration layer where Federated Learning (FL) serves as a native engine for training on distributed and private data.

Core Pillars

The platform is built to manage the complexity of decentralized AI through three core functions:

  • Orchestration A centralized control plane that orchestrates machine learning and inference workflows across thousands of distributed edge nodes where models are trained and deployed.

  • Model Aggregation Using Federated Learning techniques, the platform can securely aggregate model updates from edge nodes without transferring raw data, ensuring privacy and compliance as well as overcoming data bandwidth constraints.

  • Governance Comprehensive model versioning and management across different edge nodes and distributed regions, audit trails, telemetry, and security features ensure that all operations are transparent, compliant, and secure.

ModelOps: The Model Registry & Model Trail

Scaleout Edge provides a comprehensive ModelOps layer to manage the lifecycle of decentralized models.

Global Model Registry

The platform maintains a Global Model Registry, where generalized models are stored and managed for distribution to edge nodes. These global models can either be trained via federated learning, trained on a single edge device, or imported from external sources. In turn, edge nodes can download these models for local fine-tuning on private data.

Staging and Inference

Global models or personalized models can be staged and distributed to authorized edge nodes for local execution.

  • Model Staging – Specific model versions can be staged to authorized edge nodes for local execution. Different models can be targeted to different groups of edge nodes based on their capabilities, state or roles.

  • Inference – Inference workflows can be orchestrated on edge nodes, allowing models to make predictions locally without requiring constant connectivity to the cloud. This reduces latency and bandwidth usage while ensuring data privacy. Inference execution can be triggered from the control plane or scheduled on the edge nodes. Inference results can be logged (or artifacts pushed) and reported back to the control plane for monitoring, allowing operators to “tap-into” edge predictions without exposing raw data.

Observability: Telemetry, Performance Metrics & Client Attributes

Scaleout Edge moves beyond simple model validation by providing deep observability into both model performance and the health of the edge infrastructure itself.

Edge Telemetry

To ensure the reliability of the distributed network, the platform can log critical hardware and system metrics from edge nodes. This allows operators to monitor the “health” of the distributed network. Out of the box telemetry includes:

  • CPU and GPU utilization

  • Memory usage and disk I/O

Custom Telemetry: Users can define and add their own telemetry extensions to track domain-specific system parameters, such as device temperature and battery levels.

Experiment Metrics

The platform tracks machine learning specific metrics and experimental parameters (hyperparameters) in real time, both for aggregated models and on-device. These can be visualized in the built-in UI dashboards or obtained via the API.

Client Attributes

Each edge node can be tagged with custom attributes (e.g., location, hardware type, software version). These attributes can be used to filter and group clients for targeted model deployments or aggregations, enabling more granular control over the workflows in the distributed network.

Governance: Audit Trail & Security

As a secure lifecycle manager, Scaleout Edge ensures that all operations are auditable.

  • Audit Trail – The platform maintains explainable evidence of events that occurred in the distributed system. This includes a complete log of model updates, validations, and aggregation events leading up to any given model version.

  • Provenance Tracking – Users can trace the lineage of a model, verifying exactly which edge nodes contributed to a specific model.

  • Connection Audits – The system tracks edge node connections, disconnects, and authentication events over time to be able to detect anomalies and potential security threats.

  • Authentication & Authorization – Access is managed via Role-Based Access Control (RBAC) and JWT token-based authentication. Edge nodes require no inbound ports, ensuring a secure “outbound-only” architecture by polling the control plane.

Deployment & Infrastructure

Scaleout Edge is designed to be hardware-agnostic and scalable, supporting deployment on public clouds, private clouds, or fully on-premise air-gapped environments.

Client libraries

Scaleout Edge provides client libraries for various programming languages, making it easy to integrate edge capabilities into existing applications:

  • Python Client – A full-featured client library for Python, supporting model training, inference, telemetry logging, and secure communication with the control plane. You can easily integrate with existing ML frameworks like TensorFlow and PyTorch.

  • C++ Client – A lightweight client library for C++, optimized for performance on resource-constrained edge nodes. It provides essential functionalities for model execution and telemetry reporting.

  • Android Client – A specialized client library for Android devices, enabling mobile edge applications to participate in Edge ML workflows. TFLite models can be deployed and executed directly on Android smartphones and tablets.

Flexible Server-side Deployment Options

  • Starter / R&D – For proof-of-concepts and small fleets, the platform can be deployed via an all-in-one Docker Compose configuration.

  • Production – For large-scale operations, the platform deploys via Helm charts on Kubernetes, integrating with enterprise-grade object storage (S3) and external databases (Postgres).

Hardware Support

The client software is lightweight and compatible with a wide range of edge hardware, including:

  • x86 servers (Linux, Windows)

  • ARM64 edge devices (NVIDIA Jetson Nano/Orin, Raspberry Pi)

  • Mobile (Android)

Capability Modules

To accelerate development, Scaleout Edge offers specialized modules built on top of the core infrastructure:

  • Scaleout Vision Module A solution enabler for edge computer vision. It includes pre-packaged architectures for self-supervised learning and fine-tuning foundation models for perception tasks.

  • Security Module (Adversarial Modeling Module) A toolkit designed for security assessments, enabling simulation of adversarial scenarios such as model inversion, gradient inversion, and data poisoning attacks to audit privacy leakage.