At-a-Glance
| Challenge | How FoundriesFactory helps |
|---|---|
| Frequent AI model updates | OTA delivery of updated models and applications using container images |
| Security risks in remote deployments | All updates are digitally signed and verified end-to-end |
| Diverse hardware platforms | One Qualcomm® Linux® platform supports multiple chipsets |
| Manual update processes | CI/CD pipelines push updates automatically with rollback support |
| Scaling fleets of devices | Phased rollouts and wave deployments streamline global management |
The challenge of managing AI workloads across thousands of edge devices
Deploying AI models at the edge allows for faster inference, reduced bandwidth costs, and real-time decision-making. But scaling these deployments is complex. Developers face challenges not only in maintaining AI performance but also in ensuring security, reliability, and long-term lifecycle management.
Frequent AI model updates
AI models need to be retrained and redeployed frequently to remain effective. Teams must manage rapid iteration cycles across fleets of devices while maintaining accuracy and consistency. Without automation, this process can quickly overwhelm engineering teams.
A security focus is non-negotiable
Edge devices deployed in uncontrolled environments face ongoing security threats. Without signed and verifiable update mechanisms, attackers can exploit vulnerabilities in model delivery pipelines, firmware, or applications. The focus on security must be continuous, not a one-off step at deployment.
Diverse hardware environments
From general-purpose CPUs to specialized AI accelerators, every class of hardware demands different support and optimization. Managing this mix with custom OS builds and drivers quickly multiplies engineering effort and makes it harder to deliver updates consistently across devices.
Reliability at scale
Rolling out updates manually or via custom scripts increases the risk of failures. A single broken update can cascade into downtime, degraded model performance, or security exposure across thousands of deployed devices.
Long-term lifecycle requirements
Many industrial and enterprise AI devices are expected to run for 5–10 years. Supporting continuous updates across this time span requires a platform designed for both agility and stability.
How the FoundriesFactory platform addresses these challenges
The FoundriesFactory™ platform provides an end-to-end infrastructure for Edge AI device management, designed to simplify deployments while strengthening security and lifecycle reliability.
Linux platform built for AI at the edge
The Linux microPlatform (LmP) provides a continuously updated, minimal base OS that works across a wide range of architectures. Teams can extend it with their own BSPs, drivers, and AI frameworks without sacrificing consistency or maintainability.
Containerized AI model deployment
Applications and AI models can be packaged into containers and deployed independently of the underlying OS. This enables teams to update models without recertifying or rebuilding the entire system, accelerating iteration cycles.
Verified OTA updates
The FoundriesFactory service integrates secure over-the-air (OTA) updates for applications, firmware, and AI models. Each update is digitally signed and verified on-device, ensuring only authorized software runs in production. Built-in rollback offers resilience in the face of failures.
Embedded CI/CD pipelines
Through Git-based workflows, teams can implement DevOps for embedded AI systems. Every change triggers an automated build and deployment pipeline. This reduces human error and accelerates time-to-market.
Scalable fleet management
The FoundriesFactory service enables phased deployments and wave rollouts, allowing updates to be tested incrementally before reaching an entire fleet. This controlled approach helps validate model performance and system stability in real-world conditions while minimizing risk.
Real-World Proof
At Embedded World 2025, Qualcomm Technologies and Foundries.io demonstrated the power of combining FoundriesFactory with Edge Impulse. In the live demo, a people detection model was retrained with real-world data and redeployed to devices instantly using secure OTA updates. While this was a small-scale showcase, the same workflow is already used by Foundries.io customers to manage fleets of devices in production, proving that continuous AI improvement at scale is achievable today.
Ready to take control of your Edge AI deployments?
Launch a Community Edition factory today and experience how FoundriesFactory simplifies more secure Edge AI device management. Create a fully-featured instance in under 10 minutes and run a test OTA update — no payment details required.
Or request a technical demo with our engineers to see how FoundriesFactory can accelerate your AI product development at scale.