Blog Post

Announcing $15m Series A to Start a Data-Driven Engineering Revolution

Machine Learning, Embedded Devices, Artificial IntelligenceAnnouncing the closing of our $15 million Series A investment round led by Canaan Partners, unleashing a data-driven engineering revolution.

Zach Shelby

May 18, 2021

    Today Jan Jongboom and I are announcing the closing of our $15m Series A investment round led by Canaan Partners with Acrew Capital, Fika Ventures, Momenta Ventures, and Knollwood Investment Advisory. Canaan Partners is a great lead investor, with experience bringing 66 startups to IPO. They invest in real industries, from manufacturing and robotics to space and medical. This funding will rocket our mission to democratize ML for millions of developers and engineers deploying to billions of edge devices. Edge Impulse is already the leading machine learning platform for edge compute from Cortex-M to NVIDIA Jetson, with thousands of enterprises working on over 23,000 projects across a wide range of verticals including industrial, infrastructure, wearables, and wildlife conservation, with a global community on trajectory to reach 100,000 developers by 2022. 

    By enabling all developers to solve problems using machine learning and data, Edge Impulse has started a data-driven engineering revolution that will have immense impact on all industries. We find ourselves at a tipping point with embedded machine learning in more places than ever before, from manufacturing automation and predictive maintenance, to asset tracking and human interfaces, and beyond. We’re now having a serious conversation about how machine learning is reshaping the way all products are being built, used and maintained, ushering in a new era of engineering that uses data to drive the design of the algorithms instead of code. 

    Nearly all products and systems involving computers today are built by humans writing code, mainly by trial and error. Below is Jan’s baby Sami, already an accomplished coder. :-) Developers solve problems today using code by interpreting what they assume the input data will be, then structuring coded rules to handle the data to produce some result. Through trial and error, we improve the program by writing more code. Rule based programming quickly fails when dealing with complex multi-dimensional data such as sensor data, audio, computer vision, network traffic and much more.

    In the past we have misunderstood the power of machine learning, by focusing on artificial intelligence as the product. The big news isn’t about the latest YOLO or GPT model. The real game changer is that machine learning enables us to use custom, industry specific data to drive the design of complex algorithms, new features and personalized behavior that all developers and engineers will make use of across all industries in the coming years. 

    Data-driven engineering starts with high-quality, usually labeled data, for example on machine normal operation and failure modes from the lab. Instead of coding by trial and error, we choose appropriate feature extraction and train an ML algorithm using our dataset. Instead of hand-crafted unit testing, we use data to test the result. To improve a machine learning algorithm, the vast majority of time and effort will go into extending or improving the dataset. Data-driven engineering with tools like Edge Impulse will become a standard part of the engineering toolkit, complementing hand-crafted code and other design automation techniques. 

    We are thankful to our amazing developer community, customers, partners and team for helping make Edge Impulse what it is today!! This is the way.

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