SensiML launches free trial version of its Analytics Toolkit

December 3, 2019 Perry Cohen

SensiML Corporation, a developer of AI tools for building IoT endpoints, announced the release of a free trial version of its SensiML Analytics Toolkit and introduction of its Data Depot sample dataset repository. SensiML offers an IoT AI software program that enables developers to build endpoints 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 preview the toolkit and learn how SensiML can accelerate IoT algorithm development prior to purchasing.

The SensiML Analytics Toolkit provides benefits for both the AI endpoint product and the development process for that product. Data science is built into the toolkit, making it usable for mainstream developers. The toolkit generates code that runs on MCUs (rather than CPUs), enabling the delivery of practical AI on embedded wireless IoT devices. It also increases power efficiency with built-in, automatically generated optimizations for MCUs, DSPs, and FPGAs. All phases of design development are supported, including raw signal capture, data insight and labeling, algorithm generation, firmware code generation, and test validation and support.

Applications for the SensiML Analytics Toolkit include an array of time-series sensor usages across consumer and industrial IoT devices. Sample application datasets and documentation are
now available for exploration and use in the SensiML Data Depot dataset repository, which provides a curated set of labeled datasets for a growing list of applications.

The toolkit itself is made up of three components: the SensiML Data Capture Lab, Analytics
Studio, and the Test App. Together, these components provide a complete end-to-end
development solution. Multiple hardware evaluation platforms are supported as well, including
the QuickLogic Merced and Chilkat platforms, Raspberry Pi, the ST Sensor Tile and Nordic
Thingy to speed evaluation and subsequent design development.

For more information, visit

About the Author

Perry Cohen

Perry Cohen, associate editor for Embedded Computing Design, is responsible for web content editing and creation in addition to podcast production. He also assists with the publication’s social media efforts which include strategic posting, follower engagement, and social media analysis. Before joining the ECD editorial team, Perry has been published on both local and national news platforms including (Phoenix), (Phoenix),, Cronkite News, and MLB/MiLB among others. Perry received a BA in Journalism from the Walter Cronkite School of Journalism and Mass Communications at Arizona State university. He can be reached by email at <a href=""></a>. Follow Perry’s work and ECD content on his twitter account @pcohen21.

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