SensiML & Silicon Labs Partner on Cutting-Edge AI for IoT:

AutoML Smart Sensing Tools for the EFR32 / EFM32 Family of Low Energy MCUs

Silicon Labs Thunderboard Sense 2

SensiML has collaborated with Silicon Labs to enable AutoML-based rapid development of optimized machine learning sensor models for Silicon Labs’ energy-friendly EFR32 and EFM32 microcontrollers (MCUs).

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Introducing Community Edition:

SensiML’s New ‘Always Free’ Tier for Experimenters

SensiML is introducing a new free Community Edition tier of its industry-leading edge AI toolkit made expressly for experimenters, innovators, and product R&D teams. SensiML Community Edition provides the means to build fully functional edge IoT inference models using one’s own existing labeled or unlabeled datasets, newly captured sensor datasets using SensiML Data Capture Lab, or models built leveraging our growing Data Depot library of example and community datasets.

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SensiML Adds Arduino Nano 33 BLE Sense Support with PlatformIO

nano33 BLE Sense board

This month SensiML is pleased to announce SensiML Analytics Toolkit now provides native support for STMicroelectronics’ SensorTile.Box Development Kit for wireless IoT and wearable sensor applications. SensiML’s support for SensorTile.Box includes our most sophisticated MQTT-based device data collection firmware yet made available in open source for customers to extend and modify as necessary to suit their particular application needs.

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SensiML Starter Edition:

Buy It Once, Prototype All You Want

Beginning this month, SensiML Starter Edition is shifting from a one-time introductory subscription term to a full license without time limits. That’s right, Starter Edition will now be valid and usable for as long as you need it and actively continue using the service! To take advantage of this new license model, current and new Starter Edition customers need not do anything. All existing and prior licensed Starter Edition accounts will convert to the new buy-once, indefinite use license.

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SensiML Integrates Google’s TensorFlow Lite for Microcontrollers

SensiML Analytics Toolkit now provides pipeline support for one of the most popular open-source AI frameworks in existence: TensorFlow, specifically its TinyML variant TensorFlow Lite for Microcontrollers. The combination of SensiML and TensorFlow Lite for Microcontrollers offers best-in-class AI code generation for TinyML applications from consumer wearables to multi-sensor industrial monitoring devices.

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SensiML Adds Support for STMicroelectronics’ SensorTile.Box

This month SensiML is pleased to announce SensiML Analytics Toolkit now provides native support for STMicroelectronics’ SensorTile.Box Development Kit for wireless IoT and wearable sensor applications. SensiML’s support for SensorTile.Box includes our most sophisticated MQTT-based device data collection firmware yet made available in open source for customers to extend and modify as necessary to suit their particular application needs.

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SensiML Adds Support for QuickLogic’s QuickFeather IoT Dev Kit

QuickLogic QuickFeather - IoT EVB with low-power MCU + embedded FPGA

This week SensiML added support for the Quicklogic QuickFeather Development Kit. Noteworthy for its inclusion of Arm Cortex-M4 MCU, FPGA, and an array of sensors in a fully open-source HDK using the popular Adafruit Feather form factor, the QuickFeather makes a great IoT development platform for developers of consumer, wearable, and industrial products.

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SensiML Free Trial Edition

Amazingly simple to use, no need for large data science. SensiML Free Trial Edition includes sample datasets for rapid exploration teams. SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. Our growing library of data transformation and pattern…

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Feature (machine learning)

In machine learning and pattern recognition, a feature is an individual measurable heuristic property of a phenomenon being observed. Choosing discriminating and independent features is key to any pattern recognition algorithm being successful in classification. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The set…

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