SensiML Analytics Toolkit

Create Smart IoT Sensor Devices Rapidly Reduce Data Science Complexity

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Build Accurate AI Sensor Algorithms For the Smallest IoT Devices

AI Sensor Processing at the Extreme Edge

Create compact algorithms that execute on tiny IoT endpoints, not in the cloud.

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Production Grade Data Capture & Labeling

Collect accurate, traceable, version controlled datasets

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Rapid Prototyping in Days or Weeks

Utilize advanced AutoML code-gen to quickly produce autonomous working device code

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Code-Gen and Analysis Tools You Won’t Outgrow

Choose your interface, level of AI expertise, and retain full access to every aspect of your algorithm

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Continuous Edge-Learning Algorithms

Build edge tuning models that that customize behavior as they see more data.

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The SensiML Endpoint AI Workflow

SensiML Analytics Toolkit suite automates each step of the process for creating optimized AI IoT sensor recognition code. The overall workflow uses a growing library of advanced ML and AI algorithms to generate code that can learn from new data either the development phase or once deployed.

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Visit us at tinyML Summit 2023

Come visit us at the tinyML Summit
March 27-29 in Burlingame, CA

Exciting Demos on Show:
- HW Accelerated Keyword Recognition
- Voice ID Smart Lock
- Wizard Wand Game
- Presentation by CTO Chris Knorowski

More Info

Top 5 New SensiML Dataset Management Features

Already the best TinyML tool for managing supervised ML datasets for sensors, SensiML Data Capture Lab (DCL) now boasts a variety of new features making it easier than ever before for users to create and manage their AI/ML projects and visualize their datasets in insightful new ways. This blog explores the top 5 new dataset management and visualization features added to DCL.

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SensiML Adds Support for Silicon Labs xG24 Dev Kit

Silicon Labs' newest MG24 and BG24 SoCs with AI acceleration combined with SensiML Analytics Toolkit provides developers a major step forward in building truly smart IoT edge devices. Advanced use cases like audio classification that require complex neural network models can now be easily created by teams without AI expertise. In this multi-part blog series we will work through an example building an acoustic aware smart door lock application with AI accelerated performance.

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