We’ve updated SensiML Analytics Toolkit with a variety of new features to make it easier than ever before for users to create and manage their AI/ML projects and visualize their datasets in insightful new ways.
Opportunities to improve manufacturing processes by adding machine learning sensors at the IoT edge are rapidly emerging. Thanks to a combination of Powerful microcontrollers and multi-core SoCs like the EOS S3 on QuickLogic’s QuickAI HDK High-resolution, low-cost MEMS sensors and microphones Powerful AutoML-based AI tools like SensiML Analytics Toolkit it is possible to bring sophisticated
About six months ago we, the folks at QuickLogic, and the Avnet Hackster.io online community announced our Challenge Climate Change contest. The idea was to encourage creative smart technology projects that improve awareness, change behavior, or optimize processes to impact climate change for the better. Towards that end, we opened up the contest to developers
SensiML has collaborated with Microchip Technology to deliver ultra-compact machine learning at the IoT edge combining SensiML Analytics Toolkit with the SAM-IoT WG evaluation kit, SAMD21 MCU, and MPLAB X IDE tool suite.
Today SensiML took a leadership position in AI/ML tools for the IoT edge by announcing our new Open Source Initiative. SensiML Open Source Embedded SDK (coming later this summer) – The full library of SensiML segmenters, transforms, features, and classifiers as implemented by the AutoML and Python-based SensiML Analytics Toolkit Notebook will become available in open source format.
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).
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.
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. This week we’ve added a step-by-step boxing wearing tutorial showing how SensiML data collection, annotation, and feature preprocessing are combined with a neural network classifier using TFL Micro.
This week SensiML is releasing an all new version of its industry leading AutoML application SensiML Analytics Studio for building optimized embedded sensor algorithms for IoT devices. SensiML Analytics Studio has existed since our inception as a core component of our AI Toolkit. Previously it consisted of both a Python language interface for data scientists
SensiML offers an IoT AI software program that enables developers to build intelligent endpoints quickly and easily (up to five times faster than hand-coded solutions). Now with the availability of the free trial and Data Depot dataset repository, developers have the opportunity to easily preview the toolkit and learn how SensiML can vastly accelerate IoT algorithm development
