In this chapter, we have seen how to deploy the trained deep learning models on various platforms and devices. We have covered the steps as well as guidelines on getting the best performance for these platforms. We have seen the advantages of MobileNets for reducing the inference time with a small trade-off of accuracy.
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Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.
Read more about Rajalingappaa Shanmugamani
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Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.
Read more about Rajalingappaa Shanmugamani