How does SensiML Analytics Toolkit compare to other AI tools?

General purpose AI frameworks were made to simplify the application of machine learning algorithms to datasets across a broad array of applications. Problems ranging from credit card fraud detection to image classification or even predicting survivors of the Titanic disaster from passenger metadata have been tackled using such tools. Moreover, these tools are typically cloud-centric with adaptations to simplify their application for edge environments in some cases.

Alternatively, SensiML is a purpose-built AutoML development tool for edge devices intended for schedule-driven IoT sensor product development projects where time, investment, and efficient code are mission-critical. SensiML Analytics Toolkit has evolved over ten years originating as an Intel SW tool into an independent industry-leading multi-platform edge AI tool for building optimal models on resource-constrained IoT edge devices. To do so, our toolkit provides means for users to rapidly traverse countless combinations of segmenters, features, classifiers, and associated hyperparameters. For those not versed in data science, this AutoML search process can be done with just simple constraints letting the tool do what it does best. For those accomplished in the practice of machine learning, the tool will often surprise with results not previously considered including solutions using classifiers thought too simple to perform against more complex deep learning approaches. Nonetheless, for power users, granular control using a Python interface is included to adjust as desired the output from the AutoML results for the AI framework style experience. That’s the power and the difference of SensiML’s take on AutoML and the workflow optimization of a toolkit intended to do purposeful and scalable algorithm development for real products versus science experiments.