• Scaleout Edge introduces a dedicated Edge-Native ML Layer in the modern enterprise AI architecture that enables:
  
  • Federated learning and distributed machine learning capabilities
  • Orchestration and coordination for the entire ML lifecycle
  • Secure AI operations across distributed and mission-critical environments

• This layer bridges the gap between traditional model development and deployment, enabling enterprises to operationalize AI at the edge with robust governance and security.

• Federated Learning: Federated Learning is our private, privacy-first method for collaborative model training across edge devices, no data movement required.
• Telemetry: Real-time visibility into model performance and health across all connected devices.
• Secure Model Propagation: Safely deploy, update, and personalize models in the field without compromising data privacy.
• Centralized Orchestration: Manage and automate the full ML lifecycle from a single, secure control plane.

Product Kits

Scaleout Edge offers modular toolkits that extend the core platform with targeted functionality for specific use cases.

• VisionKit — Federated computer vision workflows for edge deployments, enabling privacy-preserving training, active learning, and real-time model adaptation. Read More »
• SecurityKit — A comprehensive ML security and privacy red teaming solution, combining automated attack simulation with expert consulting to assess and harden federated AI systems.

We design these kits to solve domain-specific challenges and accelerate AI adoption across industries such as defense, manufacturing, healthcare, and critical infrastructure.

Architecture

• ‍Control Layer (Tier 1: cloud, near edge) with global controller managing service discovery and model storage through S3;
• Model Layer (Tier 1-2: cloud, near edge, far edge) with combiners handling model aggregation and optional reducers; and
• Client Layer (Tier 2-3: near edge, far edge, device edge) with geographically distributed clients.

Components

• SDK (Open): Integrate with your existing data pipelines and ML workflows using our open SDK.
• Orchestration Backend (Licensed): Provides centralized management, automation, and monitoring of distributed models.
• UI Tools (Optional): Visualize deployments, monitor metrics, and streamline collaboration through intuitive interfaces.

‍

• Deployment options: On-premises, private cloud, public cloud, and hybrid environments supported.
• Integrations: Compatible with MLflow, TensorFlow, PyTorch, Kubernetes, and major CI/CD tools.
• APIs: RESTful APIs and SDKs for custom integration into existing ML pipelines.
• Scalability: Supports from a few to thousands of edge devices; hybrid edge-cloud deployments supported.