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Edge Computing Machine Learning

July 18, 2024

Edge Computing and Machine Learning Key Insights
byAyan PahwainTips

This blog post is powered by data from Developer Nation’s 26th global survey wave (conducted from November 2023 to January 2024), delves into the latest and most crucial developer trends for Q1 2024. With insights from over 13,000 developers across 136 countries, it’s a treasure trove of knowledge.

37% of developers who target non-x86 architectures write optimised code for Arm-based processors, making them the second most popular target behind microcontrollers (40%).

Nearly half (47%) of ML developers have deployed on-device AI solutions in the past 12 months. The top motivations for doing so are increased user privacy and faster inferencing.

The most popular on-device ML framework is Google MLKit, used by 46% of developers who deploy on-device AI solutions, followed by OpenCV (28%), PyTorch Mobile (26%), and TensorFlow Lite (25%).

The vast majority (86%) of developers working on Industrial IoT projects implement on or near-device solutions, with the most popular being on-device processing (26%), automation control (19%), and real-time analytics (17%).


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