TinyML reduces the complexity of adding AI to the edge, enabling new applications where streaming data back to the cloud is prohibitive. Sure, we can detect audio and visual wake words or analyze sensor data for predictive maintenance on a desktop computer. TinyML allows us to take advantage of these advances in hardware to create all sorts of novel applications that simply were not possible before. At SensiML our goal is to empower developers to rapidly add AI to their own edge devices, allowing their applications to autonomously transform raw sensor data into meaningful insight.
We have taken years of lessons learned in creating products that rely on edge optimized machine learning and distilled that knowledge into a single framework, the SensiML Analytics Toolkit, which provides an end-to-end development platform spanning data collection, labeling, algorithm development, firmware generation, and testing. Building a TinyML application touches on skill sets ranging from hardware engineering, embedded programming, software engineering, machine learning, data science and domain expertise about the application you are building
