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The 4 Advantages Of Using JavaScript In Machine Learning

Python has taken the center stage in recent years as the go-to language for Machine Learning (ML) with boot camps and even educational institutions offering courses in Python exclusively and sometimes with R. Python’s vast libraries for deep learning, optimized implementation, scalability etc. all serve to make it the logically preferred choice for ML. However, there are other languages that can be used to program ML like JavaScript. While it is not, as of yet, a complete substitute for Python, there are a few reasons why ML engineers should have some command over JavaScript. One of the most important reasons is that most developers across the world are well versed in JavaScript compared to Python. Conaxiom, for example, boasts of teams of coders who have a complete mastery over JavaScript. This is good news for software development companies across the board since ML and AI, the future of programming, have made their way into the JS space as well.

This article will talk about the four major advantages of using JavaScript in ML in the world today.

1. Private ML:

Today, most ML applications rely on the client-server model. Users have to send their data to servers where ML models are running. Developers can run their models on servers and make them available to users via web API’s. The client-server model, in other words, allows developers to use very large neural networks that cannot run on private user devices. However, sometimes this model is unfeasible because of the lack of privacy and it is preferable to code ML on user devices.

The good news is that ML does not always require expensive servers. A lot of models can be compressed to run on user devices and these days even mobile phones come with chips that support local deep learning. The problem, however, is Python. MacOS and a lot of versions of Linux come with Python preinstalled but you still have to install ML libraries separately. In Windows, you have to manually install Python first and then install ML libraries separately. In other words, a lot of user devices are not equipped with Python.

JavaScript, however, is preinstalled in almost all modern devices and does not need separate installation for running ML. It is guaranteed to run for all devices and users and there are a lot of ML libraries out there for JavaScript like TensorFlow.js etc. If you run those models, there will be no data sent to the cloud and there will be no need to install any additional software. Therefore, it is essential to know JavaScript when it comes to ML for wider accessibility compared to Python.

2. Speed & Customization:

ML on user devices has other advantages apart from protection of privacy. Speed, for example. Sending data through the client-server model takes time and bogs down the user experience. Moreover, users might want to run their ML models even without internet connections. In both these cases, a grasp of JavaScript and having its ML models that run on their devices is extremely useful.

Yet another advantage is model customization. When developing a ML mode that adapts to each user, one way is to store one model per user on the server and then train it on the relevant data from each user. This, however, will put an undue load on the server as it grows and sensitive data will have to be in the cloud.

The other way is to store a base model on your server and have copies on users’ devices. Then you can customize it with user data using JavaScript ML libraries. This way, user data does not have to go to the server which frees it up from undue loads and users would still be able to run their models even without connection to the server.

3. Integration in Web & Mobile Apps:

JavaScript ML is easily integrated with mobile apps while Python ML is still a little behind in this regard. There are a lot of cross-platform JavaScript mobile app development tools out there like Cordova etc. These tools allow you to write your code and then roll it out for mobile phones in both iOS and Android.

In order to ensure compatibility across different operating systems, the aforementioned tools deploy a browser object that can run JavaScript code and has the ability to be embedded in a native app of the specific operating system in question. These browser objects support JavaScript ML libraries.

Your mobile app, written in native code, can easily have your JavaScript ML code integrated with it via your own embedded browser object that you add to your app. While there are ML libraries for mobile applications specifically, you need native coding for them. JavaScript ML, however, is very flexible. If, for example, you have a running ML model on the browser, you can easily adapt it on your mobile app with negligible or no changes at all.

4. JavaScript ML on Server:

The biggest, and most difficult aspect, of ML is the training part especially in deep learning. It can be done on user devices but takes months when the neural network is big. Python is advantageous in this regard when it comes to client-server models on the server side. It is scalable and speeds up the training process since its load is distributed over server clusters. The end product can be compressed and deployed on user devices easily due to compatibility of ML libraries written in different languages.

JavaScript ML on the server side is also developing fast, however. You can run them on Node.js and TensorFlow.js has a dedicated version for servers running it. The library utilizes your server’s hardware to quicken the training process in the background. While ML with Node.js is pretty new on the scene, it is developing quickly due to the rising demand for ML in mobile and web apps.

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