Embedded ML for All Developers

Feature visualization from a 3-axis accelerometer dataset
  1. Lack of awareness. Despite several years of work of progress on Tiny ML, there is an almost total lack of awareness in industry and for developers that actually work with devices and systems that use them. Most people we tell are extremely excited about the applications, but didn’t know this was possible!
  2. ML is still too hard. Most existing tooling for ML was designed with a data scientist or ML specialist in mind, and fit poorly with production software engineering workflows. We can’t expect software developers to all become data scientists, nor companies to build expert teams just to manage data engineering. We need to make data collection, labeling, model generation and deployment available to developers and device companies at scale.
  1. Since the 1980s, we’ve coded things by hand, sometimes in assembler, and deeply integrated with low-level hardware interfaces often from the application. The focus was on machine code efficiency, real-time embedded functionality and safety.

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Zach Shelby

Zach Shelby

Zach is an entrepreneur, angel investor and technologist in the embedded space with a passion for TinyML and the Internet.