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.
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.
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.
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.
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.
This week SensiML joined a distinguished panel of healthcare, technology, and academic leaders to overview its contributions to towards an innovative COVID-19 screening technology for deployment later this year.
Scenario: Six months and $500,000 of investment into a key initiative to build a more intelligent product or streamline a strategic internal process using AI, and things are not going well.
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As we seek to spread the word on our COVID-19 AI data collection initiative, Nasdaq generously offered to promote our initiative on the “Nasdaq Tower”, a giant multi-story electronic display in Times Square in New York City. Those interested in contributing anonymous cough sound samples on our collection site https://sensiml.com/covid-19 can help build sensor insight we will make available as open source dataset and an edge AI screening solution later this year.
SensiML announces two COVID-19 initiatives:
1) SensiML Toolkit Starter Edition is now available for a limited time at $99 for a 90-day license.
2) SensiML to open-source a COVID-19 AI dataset for rapid virus screening from cough sounds. Learn how you can contribute to this project yourself.
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
Today we announced that our SensiML Analytics Toolkit supports NXP’s i.MXT RT portfolio of crossover microcontrollers and their associated i.MX RT1050 Evaluation Kit. This announcement is significant as it gives users of those devices a complete AI-based sensor algorithm development solution for IoT endpoints. With this, NXP customers using the i.MX RT crossover MCUs can
Artificial Intelligence (AI) is becoming increasingly commonplace as organizations seek to bring effective decision making and operational efficiencies to business in ways that transform how humans and machines work together. As this transformation takes shape, the advantages of edge-based AI implementations over centralized or cloud-based models is changing how AI tools are being deployed and