Optimizing TensorFlow for mobile
1. Optimizing TensorFlow for mobile
person: Let's look at a second scenario where hybrid models are necessary. Earlier, we explored how more and more applications are combining machine learning with mobile applications. Take Google Translate, for example, which is composed of several models. It uses one model to find a sign, another model to read the sign, using optical character recognition, a third model to translate the sign, a fourth model to superimpose the translated text, and a fifth model to select the best font to use. ML allows you to add some intelligence to your mobile apps, such as image and voice recognition, translation, and natural language processing. You can also apply machine learning to gain smarter analytics on mobile-specific data. For example, to detect certain patterns from motion sensor data or GPS tracking data. This is all because ML can extract meaning from raw data. So if you want to perform image recognition with your mobile app, the easiest way is to send the raw image to the cloud and let the cloud service recognize the objects in the image. However, if you have a neural network algorithm running on your mobile app, you can get labels of the objects and send them to the cloud. It's a more efficient way to collect the object labels on the cloud service. Now let's say you perform motion detection with your mobile app. In this case, you can run a neural network algorithm to extract a feature vector from the sensor data. The numbers in the feature vector represent the signatures of each motion. This means you don't have to send the raw motion data to a cloud service. Also, by applying machine learning to mobile apps, you can reduce network bandwidth and get faster response times when communicating with cloud services. It's important to note that you often can't use the microservices approach for mobile devices, because they can add unwanted latency. You can't delegate to a microservice as you can when running in the cloud, so you'll now want a library, not a process. In these types of situations, it's best to train models in the cloud and carry out predictive modeling on a device. This means embedding the model within the device itself.2. Let's practice!
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