As AI models and edge devices evolve, the need for efficient AI inference at the edge has become more pronounced. By bringing computation closer to data sources, businesses can reduce latency, enhance real-time decision-making, and reduce dependency on cloud infrastructure. However, managing AI model deployment and continuous updates remains a significant challenge. Golioth, an IoT platform specializing in device management, has stepped up to address this need, offering seamless integration, flexibility, and powerful tools for managing AI models on edge devices.

Golioth’s platform is designed to make the deployment of AI models on edge devices both simpler and more effective. It provides key features such as over-the-air (OTA) updates, remote procedure calls (RPCs), device logs, and robust data services. With the recent inclusion of AI capabilities, Golioth now enables easy ingestion of data for model training, as well as efficient routing of data to hosted inference platforms. This creates a comprehensive solution for managing edge AI deployments at scale.

One of the platform's standout features is the seamless integration of AI models. Golioth’s OTA service allows businesses to manage and deploy AI models efficiently, ensuring that devices in a fleet are running the latest versions of models. This capability ensures that businesses can continuously improve their AI solutions without needing to overhaul entire systems or disrupt operations.

A significant recent development in Golioth’s platform is its collaboration with Qualcomm and Foundries.io. The partnership has resulted in a powerful integration that leverages Qualcomm's AI models on edge devices, specifically using the Qualcomm RB3 Gen 2 development kit. This kit, built around the Qualcomm QCS6490 SoC, includes key features such as the Qualcomm Kryo 670 CPU, Adreno 643L GPU, and Hexagon DSP, all designed to accelerate AI workloads on edge devices.

The combination of Qualcomm's robust hardware and Golioth’s data routing capabilities allows businesses to deploy AI models, such as the YOLO ("You Only Look Once") object detection model, on devices with high processing power. Once the model is deployed on the device, inference results, including image captures and other data, are streamed to the cloud for further analysis.

The integration of Golioth’s Firmware SDK into the Qualcomm RB3 Gen 2 enables real-time logging and monitoring of deployed models. It provides visibility into which model is running on each device and ensures that inference results and raw data are transmitted back to the cloud seamlessly. This integration also simplifies model updates; when new models are available, Golioth facilitates OTA updates that only require downloading the new model and labels—rather than replacing the entire application.

The flexibility of Golioth’s platform and Qualcomm’s AI models has opened up a wide array of potential use cases across various industries. In industrial manufacturing, for example, AI-powered edge devices can detect defects in real time, predict machinery failure, and ensure safer working conditions. Similarly, in smart cities, AI can optimize vehicle and pedestrian movement, monitor environmental conditions such as air quality, and reduce energy consumption.

Agriculture stands to benefit as well, with AI applications capable of monitoring crop health, improving irrigation strategies, and maximizing yield. Additionally, in the energy, waste, and utilities sectors, AI models can be used to predict energy consumption trends, monitor water quality, and track the fullness of waste receptacles, providing valuable insights for more efficient management.

Golioth’s data plane is designed to route data efficiently to multiple destinations. For instance, inference results can be transformed into JSON payloads and stored in an integrated time-series database, while image data can be sent to blob storage for later use, such as offline model training or fine-tuning. This efficient use of cloud resources ensures that businesses can scale their AI operations without incurring unnecessary overhead.

As businesses continue to adopt AI for edge device management, Golioth’s platform offers a compelling solution that simplifies deployment, management, and scaling. With its seamless integration of Qualcomm’s AI models and its powerful data services, Golioth is positioning itself as a leader in the rapidly growing edge AI space. Whether in manufacturing, smart cities, or agriculture, Golioth’s IoT platform is enabling businesses to unlock the full potential of AI at the edge.

The ability to remotely manage AI models, perform OTA updates, and route data efficiently ensures that businesses can stay ahead of the curve and respond quickly to changing needs. Golioth’s approach to edge AI is setting new standards for flexibility, scalability, and real-time performance.