Create compact algorithms that execute on tiny IoT endpoints,
not in the cloud
Collect accurate, traceable, version controlled datasets
Utilize advanced AutoML code-gen to quickly produce autonomous working device code
Choose your interface, level of AI expertise, and retain full access to every aspect of your algorithm
Build edge tuning models that that customize behavior as they see more data.
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.
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.More Info
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.More Info
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).More Info