In the past few years, Python has been the preferred and primary language in machine learning and deep learning. Most of the tutorials in this field only offer Python or, in parallel, the R programming language. The reasons for Python’s popularity include having a complete list of machine learning and deep learning libraries, optimal execution of operations, scalability, and diverse features.
But is Python the only option for machine learning and programming machine learning applications?
Table of Contents
1. Private Machine Learning
Most machine-learning applications rely on client/server architectures. The client/server architecture has many advantages. Developers can run their models on servers and make them available to application users through web APIs. And in this way, use large neural networks that do not run on application devices.
But in many cases, it is preferable to perform machine learning inference on the user’s device. For example, for privacy reasons, users may not want to send their photos, private messages, and emails to a server running a machine-learning model.
Fortunately, not all machine learning applications require expensive servers. Many models can be squeezed into user devices, and mobile phone manufacturers are equipping their devices with chips that can support local deep-learning inference mechanisms.
The problem is that machine learning with Python is not supported on many devices. Python is installed by default on MacOS and most versions of Linux. But the user must install the machine learning libraries separately. Windows users also have to install Python manually, and mobile operating systems have very poor support for Python interpreters.
2. Fast and customized ML models
This, on the one hand, keeps the data on the users’ devices and removes the need to send them to the server. On the other hand, it frees up server resources by avoiding sending a load of inference and additional training to the cloud memory. Users can still use machine learning features even when they are disconnected from your servers.
3. Integration of machine learning in web and mobile applications
There are other machine learning libraries for mobile apps, such as TensorFlow Lite and Core ML. Of course, they require native coding on the mobile platform you’re developing your app for. It’s also possible that if you’ve already implemented a browser version of your machine learning app, you can easily port it to your mobile app with little or no changes.
One of the main challenges of machine learning is training models. Especially in deep learning, where learning requires a lot of computation. Deep learning models can be trained on user devices. Of course, if the neural network is large, this process may take weeks or months.
But behind the scenes, the library uses your server’s special hardware to speed up training and inference. Machine learning with Node.js is relatively new, but developing rapidly. Because there is a growing interest in adding machine learning capabilities to web and mobile applications.