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
Production Grade Data Capture & Labeling
Collect accurate, traceable, version controlled datasets
Rapid Prototyping in Days or Weeks
Utilize advanced AutoML code-gen to quickly produce autonomous working device code
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
Continuous Edge-Learning Algorithms
Build edge tuning models that that customize behavior as they see more data.
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.
SensiML Launches Open Source Initiative
May 10, 2021 – SensiML announces it has launched an Open Source Initiative to accelerate the adoption of TinyML smart sensing IoT applications. The initiative builds upon SensiML’s existing efforts to design flexibility, transparency and efficiency into its product suite by giving developers control and insight over vital aspects of their ML workflow, tools, data, and resulting models.
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TensorFlow Lite + SensiML =
Better, Faster Model Creation
Head over to TensorFlow's blog site and read the details on how TensorFlow Lite + SensiML AutoML can deliver smaller models with improved performance leveraging the workflow benefits of SensiML's scalable dataset collection and labeling tools as well.
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SensiML & Silicon Labs Partner on Cutting-Edge AI for IoT
March 25, 2021 – 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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