2.04.2020

Python vs. JavaScript. Which One Is Better For Your Next Machine Learning Project?

Article by
author
Miłosz Kaczorowski
7 min read

Machine intelligence has rapidly moved from experimental to applied technology, and the level of its adoption is growing steadily, also among small and medium businesses. 2020 is poised to be a landmark year for the development of AI and machine learning solutions, so it’s the right moment to tap into the unique opportunities they present.

 

According to industry research, while enterprises invest in ML primarily to reduce costs and reduce manual work, startups and small businesses harness the technology mainly to enhance customer-facing functions and gather insights that allow them to refine services, increase customer satisfaction and retention, and gain a competitive edge. Those benefits of machine learning (for more, see our free guide to AI) are up for grabs for your business, too. 

 

The best way to launch a machine learning project is to start small. First, you need to choose the programming language that’s the most suitable for the implementation of your idea. This decision will impact key considerations of your project, such as cost, resources, and time. To help you, we look into two popular choices when it comes to machine learning, namely Python and JavaScript. Let’s see how they fare against each other.

What Is JavaScript?

 

JavaScript (JS) consistently ranks first as the most widely-used programming language among developers. It is an industry-standard frontend scripting language that’s best suited for making static websites dynamic, which cannot be attained with standard HTML and CSS. What this means is that when an object on a website, for example, a button, is created in HTML, JavaScript makes it possible to animate it and interact with it. Because of its common application in web development, JavaScript is everywhere, to the point that it comes built-in in  all web browsers, and doesn’t require downloading any compilers or extra software.

 

Examples of JavaScript: When you are liking a friend’s post on Facebook, scrolling photos on Instagram, or using your social credentials to log in to a portal, that’s JavaScript standing behind those features.

Can JavaScript Be Used For Machine Learning?

 

A short answer is ‘Yes.’ However, the real question should be, whether it’s the most suitable choice for ML applications. 

 

When implementing a machine learning project, a few significant factors come into play. First of all, the number of available code libraries, so pre-existing sets of code routines that may be reused in different applications. The more of them are available, the more streamlined the implementation. 

 

While most JavaScript machine learning libraries are quite recent, especially when compared to Python, they are advanced enough to power some significant ML use cases. What’s more, new ones are continuously being added, focusing especially on computational data, data analysis pipeline (e.g., data generation), and performance optimization. In this context, it’s also important to mention that frontend JS machine learning libraries are usually applied for existing, pre-trained models because of the computational costs while training new ML models is done in backends like Node.js

 

The second important factor in favor of JS is community support, which often proves essential, especially in complex or pioneering projects. The JavaScript ecosystem of over 10 million developers is constantly adding and evolving JS libraries that make the implementation of ML algorithms and neural networks much easier. 

Examples of JavaScript Applications in Machine Learning

 

As mentioned above, JavaScript is primarily used for creating dynamic web pages. Therefore, its application in machine learning is mostly related to running ML models in web browsers (for example, to create an app that allows devices to recognize and identify objects appearing in the camera). One important feature of JavaScript that makes it suitable for some ML projects is the fact that it can run on most platforms and device types, lessening compatibility issues. 

 

A popular open-source library that enables the deployment of machine learning programs in the browser is TensorFlow.js. It allows running existing models in the browser, train ready models with your own data, and develop new machine learning models directly in the browser. Another commonly used library is Brain, which enables the creation and training of neural networks and loading them onto a browser, e.g., to recognize color contrast. 

What Is Python?

 

While it is not as widespread as JavaScript, Python is one of the fastest-growing programming languages in the world, surpassing Java, C#, and Ruby, and one of the developers’ favorites. It is widely used in a range of applications, from GUI-based desktop and web applications, through business solutions, up to data analysis engines.

 

Python is an open-source, general-purpose programming language, praised for high readability, productivity, and speed. Python is often used as a main backend language or an extension language for web applications written in other languages, and it is also a frequent choice when it comes to the development of automation, data mining, and machine learning platforms.

 

Examples of Python: Python is one of the official server-side languages used by Google; it is also used inside Instagram and Facebook infrastructures. Also, Quora, Netflix, and Spotify use it extensively, mainly to power their data analysis capabilities and backend services. 

Is Python The Best For Machine Learning?

 

AI and machine learning development are fast-progressing, and there’s no shortage of their innovative applications in business. Some prominent examples include image classifiers, social media sentiment analytics, chatbots, predictive engines, or personalized recommendations (look here for more examples). To implement these tools and functionalities, data scientists, programmers and DevOps use a combination of programming languages, Python included.

 

For years, Python has been the language of choice for ML implementations. It provides a comprehensive library of packages with in-built functions that facilitate data analysis and processing, cleansing, modeling, visualization, and so on. These include TensorFlow, Keras, and Theano, all of which make it easy to implement various machine learning features. Many developers consider Python the preferred language for ML projects also because of its capability of interacting with other languages and platforms, and robust data handling capacity. 

 

From a business standpoint, Python is used for machine learning projects for several reasons. First of all, it’s highly productive thanks to its design and has a ton of ready to use packages, which positively impacts the speed of implementation. Secondly, Python has a large community of developers and supporters. It’s estimated that there are more than 8 million Python coders around the world. This makes it easier to find people with the right skills to launch the project quickly. The extensive community support also enables daily code enhancements and regular creation of new libraries and packages that further accelerate the pace of development. 

Python vs. JavaScript: Questions to Ask Before You Choose

 

The selection of the right programming language is the first step to the implementation of any machine learning project. Therefore, if you cannot choose between Python vs. JavaScript, here a few questions that may ease your way into the decision:

 

  • What is your app or model supposed to do? What environment will it run on? For example, if models leveraged by your application are to be frequently and extensively used, you might want to apply a JavaScript in-browser ML solution to lessen the cost of servers. If your specific ML problem has been already solved with Python, it will probably make more sense to use it instead to deliver your solution faster. 

  • What is your existing technology stack? Which frameworks are your teams using? Aligning the programming language with the existing technology stack usually leads to faster and more streamlined development. Choose a language that best matches your current technology capabilities. 

  • Are there any specific libraries and tools that are industry-standard or readily available? Python offers more data science packages and built-in functions, so the implementation may be a bit faster than in JS. However, new libraries for JavaScript are constantly emerging. For some usages, the language offers a mature and extensive catalog of tools that enable smooth development. 

  • Do your developers know the language? How fast can you hire experts if external support is required? In the case of JavaScript and Python, this isn’t that much of an issue, as they belong to the world’s most popular programming languages. However, remember that data science projects have their own special requirements. To avoid bottlenecks in project implementation, hire only pre-vetted teams of developers who combine programming language proficiency with the understanding and experience in machine intelligence development.

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In Conclusion

 

Which language is the winner in our Python vs. JavaScript race? As always, it’s impossible to give a definitive answer. It all depends on your demands, type of solution, and available resources. 

 

Although machine intelligence adoption continues to grow, AI and ML projects still involve some level of complexity and require expert know-how. The good news is that now, you don’t have to build your own arsenal of data analytics expertise to tap into the value of machine learning for your business. If you don’t want to grapple with ML alone, hire a dedicated, ready-to-deploy tech team who will help you choose the best programming language for your needs, and build innovative solutions that will keep your company ahead of the game.

 

JavaScript or Python for machine learning projects?

 

An increasing number of businesses are experimenting with AI and machine learning, incorporating data analytics and automation into their long-term business strategy.

Before you start your first ML project, choose the programming language that will best match your requirements.

 

JavaScript

One of the most popular languages in the world, used mainly for creating dynamic web pages. 

PROS

  • It allows using ML anywhere, e.g., in wearables or mobile apps.
  • JavaScript makes machine learning accessible to web and front-end developers.
  • It offers a powerful, open-source Tensorflow.js library that makes it possible to define, test, and run ML models in web browsers.
  • JS has the largest community of developers of all languages, which makes it easier to find the right team to work on your project.

CONS

  • The number of available data science packages and built-in functions is small when compared to Python.
  • The community of JS developers with specific data science expertise is still emerging.

 

Python

A versatile, multi-paradigm programming language that is used in the backend of many large and popular applications today.

PROS

  • Python offers a wide choice of libraries and in-built functions for data analytics, scientific computing, and machine learning.

  • The world’s largest companies such as Google, Facebook, Microsoft, or Netflix use it in their Machine Learning projects. 

  • It’s one of the fastest-growing programming languages, and its use in machine learning is extensive.

  • It is a highly readable, multi-paradigm language that supports productivity and speed of development.

CONS

  • Python is not inherent in the mobile environment.

  • You cannot use it inside browsers as you can JavaScript.

 

Which one is better for machine learning? Python or JavaScript?

 

On the surface, Python seems to be the language of choice for most machine learning projects. However, this doesn’t necessarily have to be true in all cases. In machine learning web applications leveraging frontend components like camera inputs, going with JavaScript may turn out more practical. 


As always, there are a number of aspects to consider before reaching the final decision. To get support, consult a trusted team of technology experts with previous experience in implementing AI and ML projects in both languages.

author

Miłosz Kaczorowski

Co-Founder of Ideamotive - custom software development company. Highly skilled in Ruby on Rails, JavaScript and Linux System Administration. Experienced in implementing effective web apps.

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