Streamlining Embedded Machine Learning
Daniel discusses the challenges of integrating machine learning into embedded engineering workflows, including the need to detect defects before shipping products and optimizing models for a wide range of devices. He also highlights the importance of building a pipeline that can convert models into optimized C++ code, making it easier for developers to deploy and test their creations on various hardware.In this clip
From this podcast
The Gradient
Daniel Situnayake: AI on the Edge
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What problems do developers face when building AI applications as discussed in the episode Daniel Situnayake: AI on the Edge and the clip Streamlining Embedded Machine Learning?
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What problems do developers face when building AI applications in the context of the episode Daniel Situnayake: AI on the Edge and the clip Streamlining Embedded Machine Learning?