Although regretfully most trade-fairs are suspended at the moment, we are happy to share a recent solution that was build using our patented software: simple and elegant, local, event booth analytics. Together with Advantech we setup an interactive demo of our technology during the recent Vision summit.
Our AI manager ensures that our clients can run AI/ML models securely on various edge devices and makes it easy to monitor performance and innovate new models. We have successfully installed the AI manager on a large number of Edge devices (i.e., The Advantech ICR32xx and 42xx series, the Siemens IOT2050, etc.). For developers we are now making the AI manager available for local experimentation!
We are happy to share the newest addition to our solutions: We are now able to fully integrate our product quality control solution with a Jaka cobot. Co-developed with HPS, we are making it super easy to use AI and machine vision models modularly and securely in flexible production environments.
The Eindhoven start-up Scailable has developed software that makes it possible to process large amounts of data no longer ‘in the cloud’ but ‘on the edge’ into usable information. The cost savings, improved security and faster speed achieved by processing data where it is collected will play a major role in accelerating the adoption of Artificial Intelligence (AI). The Brabant Development Company (BOM), together with Volta Ventures and the Rabobank Innovation Fund, is investing EUR 650,000 in the further development and market introduction of the software.
How AI on the Edge (provided by Scailable) and LoRa (provided by KPN Things) jointly enable novel (I)IoT applications.
This tutorial describes a demo we recently presented during the 19th KPN Startup Afternoon; we demonstrated how we can use the Scailable Edge AI deployment platform to deploy a fairly complex AI model (a deep neural network recognizing human emotions) to a small ESP32 device. Subsequently, we used an Arduino MKR WAN 1310 and KPN Things to send the resulting emotions to the cloud via LoRa (find a short video here).
Deploy or update AI models in one click to any device. Manage a multitude of devices. Orchestrate and log your model deployments safely and securely. Fast, easy, and secure.
Image processing is one of the prominent uses of AI models. With the Scailable platform we can easily deploy image processing models anywhere. For example, with a click of a button, we were able to deploy a face blurring deep neural netwerk to the browser. Deploying such models in the browser (as opposed to in the cloud) makes that potentially private data never leaves the user’s local machine.
ONNX has been around for a while, and it is becoming a successful intermediate format to move, often heavy, trained neural networks from one training tool to another (e.g., move between pyTorch and Tensorflow), or to deploy models in the cloud using the ONNX runtime. However, ONNX can be put to a much more versatile use: ONNX can easily be used to manually specify AI/ML processing pipelines, including all the pre- and post-processing that is often necessary for real-world deployments. In this tutorial we will show how to use the
onnx helper tools in Python to create a ONNX image processing pipeline from scratch and deploy it efficiently.