“I had the pleasure of working with Dan on several projects, including the development of the Python SDK at Edge Impulse. Daniel's strong foundation in machine learning and his ability to provide clear customer stories and detailed code reviews were instrumental in creating a robust and user-friendly interface. His passion for edge AI and talent for simplifying complex concepts greatly benefited our team. Daniel's leadership of the ML team at Edge Impulse resulted in innovative models and systems that empower users to build edge AI products addressing real-life problems. His ability to articulate the technical vision and motivate the team was key to our success, fostering a collaborative and growth-oriented environment. I am grateful for the opportunity to have worked with Daniel and highly recommend him for his expertise, leadership, and visionary thinking in the field of machine learning and edge AI.”
Daniel Situnayake
Mount Pleasant, South Carolina, United States
3K followers
500+ connections
About
Daniel Situnayake is Director of Machine Learning at Edge Impulse, and a technologist…
Activity
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Our team will be at #CES2025 and is looking forward to meeting with anyone interested in building edge AI applications! There are demos featuring…
Our team will be at #CES2025 and is looking forward to meeting with anyone interested in building edge AI applications! There are demos featuring…
Shared by Daniel Situnayake
Experience
Education
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Birmingham City University
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Received the TIC Undergraduate Computer Networking Prize as the top performing Networking student of 2007.
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Volunteer Experience
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City Contact for San Francisco Bay Area
Labour Party
- 2 years
Politics
The Labour Party is the UK's largest political party. I run meetings and events for members resident in the San Francisco Bay Area.
Publications
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AI at the Edge: Solving Real World Problems with Embedded Machine Learning
O'Reilly
Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to flexible embedded Linux devices--for applications that reduce latency, protect…
Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (IoT) devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to flexible embedded Linux devices--for applications that reduce latency, protect privacy, and work without a network connection, greatly expanding the capabilities of the IoT.
This practical guide gives engineering professionals and product managers an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level roadmap will help you get started.Other authorsSee publication -
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
O'Reilly
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size--small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. As of early 2022, the supplemental code files are available at https://oreil.ly/XuIQ4.
Pete Warden and Daniel Situnayake explain how you can train…Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size--small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. As of early 2022, the supplemental code files are available at https://oreil.ly/XuIQ4.
Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.Other authorsSee publication
Languages
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English
Native or bilingual proficiency
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Join now to viewMore activity by Daniel
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Congratulations to Reka Meszaros and Hanna Selim at Aalborg University on their healthcare AI project using Edge Impulse, recently demonstrated to…
Congratulations to Reka Meszaros and Hanna Selim at Aalborg University on their healthcare AI project using Edge Impulse, recently demonstrated to…
Shared by Daniel Situnayake
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I'm looking forward to being a keynote speaker at the Generative AI for Edge Computing Symposium, at the AAAI 2025 Spring Symposium from March 31 to…
I'm looking forward to being a keynote speaker at the Generative AI for Edge Computing Symposium, at the AAAI 2025 Spring Symposium from March 31 to…
Shared by Daniel Situnayake
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