Originally posted on thenextweb.
In the past few years, Python has become the preferred programming language for machine learning and deep learning. Most books and online courses on machine learning and deep learning either feature Python exclusively or along with R. Python has become very popular because of its rich roster of machine learning and deep learning libraries, optimized implementation, scalability, and versatile features.
#1.Private machine learning
Most machine learning applications rely on client-server architectures. Users must send their data where the machine learning models are running. There are clear benefits to the client-server architecture. Developers can run their models on servers and make them available to user applications through web APIs. This makes it possible for developers to use very large neural networks that can’t run on user devices.
In many cases, however, it is preferable to perform the machine learning inference on the user’s device. For instance, due to privacy issues, users may not want to send their photos, private chat messages, and emails to the server where the machine learning model is running.
Fortunately, not all machine learning applications require expensive servers. Many models can be compressed to run on user devices. And mobile device manufacturers are equipping their devices with chips to support local deep learning inference.
But the problem is that Python machine learning is not supported by default on many user devices. MacOS and most versions of Linux come with Python preinstalled, but you still have to install machine learning libraries separately. Windows users must install Python manually. And mobile operating systems have very poor support for Python interpreters.
#2. Fast and customized ML models
On the one hand, this would keep data on users’ devices and obviate the need to send them to the server. On the other hand, it would free up the resources of the server by avoiding to send extra inference and training loads to the cloud. And users would still be able to use their machine learning capabilities even when they’re disconnected from your servers.
Client-side machine learning allows developers to run customized models on user devices
#3. Easy integration of machine learning in web and mobile applications
One exception is React Native, a popular cross-platform mobile app development framework that does not rely on webview to run applications. However, given the popularity of mobile machine learning applications, Google has released a special version of TensorFlow.js for React Native.
One of the main challenges of machine learning is training the models. This is especially true for deep learning, where learning requires expensive backpropagation computations over several epochs. While you can train deep learning models on user devices, it could take weeks or months if the neural network is large.
This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech, and what we need to look out for. You can read the original article here.