Qeexo also announces new features and support for the Renesas RA Family of 32-bit MCUs
MOUNTAIN VIEW, CALIF. (PRWEB) FEBRUARY 27, 2020 Qeexo today announces several new features for its AutoML product, including support for Amazon Web Services (AWS) and tools designed to give Qeexo AutoML users greater flexibility and performance in the collection and analysis of sensor data:
- Support for AWS: Users of Qeexo’s AutoML now have the option to install the product locally on their private server, or to access it via AWS. This allows for increased scalability to serve more users and build more complex models faster by leveraging the Cloud.
- More machine learning models: Users can now select more machine learning algorithms (both deep-learning and non-deep-learning) when building models.
- Data and model visualization: Users can visualize the collected/uploaded sensor data, and also see more details for each model, including various graphs and charts that increases explainability.
- Support for microphone sensors: Users can now collect and analyze data from a device’s microphone, greatly augmenting the supported use cases (e.g. keyword spotting).
Qeexo also announces that its Qeexo AutoML product now supports the Renesas RA Family of Cortex-M MCUs. Qeexo AutoML is a fully-automated, end-to-end platform that builds lightweight machine learning solutions at the Edge. The Renesas RA Family of 32-bit MCUs is designed to help device developers create next-generation secure and low-power IoT devices. The combination will enable developers to rapidly build machine learning solutions for Edge devices using Renesas hardware and Flexible Software Package (FSP).
International Data Corporation (IDC) estimates that there will be 41.6 billion connected IoT devices, or “things,” generating 79.4 zettabytes (ZB) of data in 2025.
“As the number of IoT devices grows, companies increasingly struggle to make sense of the vast amount of data that they generate,” said Sang Won Lee, CEO of Qeexo. “With Qeexo AutoML, we allow even non-experts to rapidly build machine learning models that run at the Edge, in order to analyze this data and generate actionable insights in real-time.”
“Integrating our RA MCUs with an automated machine learning tool like Qeexo AutoML will greatly augment the user experience and applicability of our products. It will also make it easier for our joint customers to navigate the process from prototyping to production without prior expertise or much experience with artificial intelligence and machine learning,” said Kaushal Vora, Director of Strategic Partnerships & Global Ecosystem at Renesas Electronics Corporation. “We will continue our collaboration with Qeexo to enable even more Renesas hardware on Qeexo AutoML.”
Qeexo AutoML greatly simplifies the development process for machine learning solutions, allowing companies to create machine learning models without having to invest in expensive, in-house machine learning teams, resulting in huge time and cost savings. Qeexo AutoML can automatically generate models that can run locally on environments constrained by power and memory, such as IoT devices, wearables, and automotive sensors.
Qeexo is the first company to automate end-to-end machine learning for embedded Edge devices (Cortex M0-M4 class). Our one-click, fully-automated, Qeexo AutoML platform allows customers to leverage sensor data to rapidly build machine learning solutions for highly constrained environments with applications in mobile, IoT, wearables, automotive, and more.
Delivering high performance, solutions built with Qeexo AutoML are optimized to have ultra-low latency, ultra-low power consumption, and an incredibly small memory footprint.
As billions of sensors collect data on every device imaginable, Qeexo can equip them with machine learning to discover knowledge, make predictions, and generate actionable insights.
Spun out of Carnegie Mellon University, Qeexo is venture-backed and headquartered in Mountain View, CA, with offices in Pittsburgh, Shanghai, and Beijing. To learn more, visit http://automl.qeexo.com.