Siddhika worked on groundbreaking projects at Microsoft and Apple before co-founding Tetra AI Hub, later acquired by Qualcomm, where she now serves as the Senior Director of Project Management. Our guest explains why she switched focus from cloud to edge computing, why there’s a need to both increase compute on edge devices and develop more efficient AI models, and how the conversation around edge has evolved since the very first murmurs.
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Today we are joined by Siddhika Nevrekar, an experienced product leader passionate about solving complex problems in ML by bringing people and products together in an environment of trust. We unpack the state of free computing, the challenges of training AI models for edge, what Siddhika hopes to achieve in her role at Qualcomm, and her methods for solving common industry problems that developers face.
Key Points From This Episode:
“Ultimately, we are constrained with the size of the device. It’s all physics. How much can you compress a small little chip to do what hundreds and thousands of chips can do which you can stack up in a cloud? Can you actually replicate that experience on the device?” — @siddhika_
“By the time I left Apple, we had 1000-plus [AI] models running on devices and 10,000 applications that were powered by AI on the device, exclusively on the device. Which means the model is entirely on the device and is not going into the cloud. To me, that was the realization that now the moment has arrived where something magical is going to start happening with AI and ML.” — @siddhika_
Links Mentioned in Today’s Episode:
Sama provides ML Professionals and AI team Leads with an indispensable solution for Computer Vision data labeling.
