In this chapter, we discussed how to deploy machine learning models, especially neural networks, to mobile and cloud platforms. We examined that, on these platforms, we usually need a customized build of the machine learning framework that we used in our project. Mobile platforms use different CPUs, and sometimes, they have specialized neural network accelerator devices, so you need to compile your application and machine learning framework in regards to these architectures. The architectures that are used for cloud machines differ from development environments, and you often use them for two different purposes. The first case is to use powerful machine configuration with GPUs to accelerate the machine learning training process, so you need to build your application while taking the use of one or multiple GPUs into account. The other case is using a cloud machine for inference...
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You're reading from Hands-On Machine Learning with C++
Kirill Kolodiazhnyi is a seasoned software engineer with expertise in custom software development. He has several years of experience building machine learning models and data products using C++. He holds a bachelor degree in Computer Science from the Kharkiv National University of Radio-Electronics. He currently works in Kharkiv, Ukraine where he lives with his wife and daughter.
Read more about Kirill Kolodiazhnyi
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Kirill Kolodiazhnyi is a seasoned software engineer with expertise in custom software development. He has several years of experience building machine learning models and data products using C++. He holds a bachelor degree in Computer Science from the Kharkiv National University of Radio-Electronics. He currently works in Kharkiv, Ukraine where he lives with his wife and daughter.
Read more about Kirill Kolodiazhnyi