Ruby on Rails (RoR) is a framework written in the Ruby language. Since its release in 2004, it has been used by heavy-hitting companies such as Twitter, Shopify, and AirBnB. While RoR has proven itself to be a great language for building incredible applications, some developers might be wondering if RoR is a good machine learning language. In this article, we'll list the pros and cons of RoR as a machine learning language and how these pros outweigh the cons.
Is Ruby on Rails a good machine learning language?
Machine learning is a branch of data science that allows computers to learn without being specifically programmed. It often involves having computers predict unknown results after being fed information from related bulky data sets.
RoR is most often used for web development and scripts. But it has also been used in e-commerce application development, social media platforms, and streaming applications, among others.
RoR provides a framework that simplifies development by reducing the number of choices a programmer has to make while steering the entire project in the right direction. This makes it an excellent framework for new developers in particular.
These characteristics should also make RoR the perfect machine learning language. However, there are also several detracting factors to RoR, often resulting in developers opting to use alternative methods. Even with these detracting factors, we would still
Why Ruby on Rails works for machine learning
Ruby on Rails has several characteristics that make it a good choice as a machine learning language.
It offers flexibility and adaptability
Development using RoR is incredibly flexible and provides high levels of adaptability. Because it’s constantly being updated, applications built using Rails should always be up to date with the latest standards and technical requirements.
This means that RoR has found use across a wide range of industries, from telecoms to e-commerce.
RoR lets developers remove specific elements at any stage of, or even after, development. These can be redefined and reintegrated or replaced with alternative modules. This allows for the easy addition of features, enabling companies to be as flexible and adaptable as they need to be.
The RoR programming language is very flexible in itself. It features a clean syntax that is easy to learn, making it a popular choice for new developers. It also has a host of modules that can be utilized as needed.
It provides great documentation
There is a wealth of documentation available to support development for RoR. As an open-source framework, Rails has been distributed for free around the world, giving many developers the opportunity to share their ideas and cases.
Resources are easily located in the many online RoR communities. There’s enough documentation available for even the newest developers to get to grips with the entire structure of RoR.
It helps build faster prototypes
RoR has hundreds of ready-to-use modulus and plug-ins for developers, saving them from writing code from scratch. Due to the number of ready-to-use modules, developers can build a prototype 40% faster than with other programming languages.
In addition to these modules, Rails is object-oriented, which means that the process of creating elements in the application is greatly simplified. The code is clean, and the modular nature means that changes can be made quickly. What’s more, automated software testing is made much easier, which helps to speed up development time.
Plus, due to how long RoR has been around, it’s easy to find tools to integrate with your prototype. For example, Airbrake’s error monitoring tool seamlessly integrates with RoR, perfect when you need to monitor for errors during testing and deployments.
It’s easy and convenient to connect with other services
Ruby on Rails is commonly used by many startups due to its ease of use and simple programming language.
Developers across the Rails network use standardized file storage and programming conventions. This makes projects more readable and keeps them structured, saving time when forging connections.
For example, you may combine technologies, building your framework with RoR and connecting to TensorFlow or Python as a microservice for machine learning. With Ruby on Rails, there are several reliable ways to do this which won’t disrupt core integrity.
Rails has an active community
Rails has a dedicated and widespread community that offers a great source of practical and theoretical support. In fact, several of the more dedicated members will often organize meetups, video conferences, and webinars, providing a fantastic educational resource.
Several communities are active on Discord, including the Official Ruby on Rails Discord server and the GoRails Discord server. There is also the official Ruby on Rails forum hosted on Discuss and the Ruby on Rails Twitter community.
As mentioned before, RoR is an open-source framework. Because it has been built collaboratively, developers from around the globe have contributed to new ideas and applications for the language while also helping to find solutions to problems, which developers new to RoR can take advantage of.
Because RoR is a comparatively mature framework, there is a wealth of literature available from generations of developers who have experimented with the language. You can be confident that if you run into a problem when using RoR, somebody, somewhere, will have experienced the same issue and shared their solution.
It’s a stable solution trusted by famous companies
Ruby on Rails’ flexibility, adaptability, and speed of development have made it the framework of choice for several companies. Although its usage has declined somewhat in the last couple of years, these companies help to keep RoR alive.
Here are a few companies that you might have heard of that use RoR to develop their applications.
Love it or hate it; Twitter has revolutionized how content is shared online since its launch in 2006. Twitter was developed using a combination of Ruby, Ruby on Rails, and jQuery, giving the developers the necessary tools to build and launch their platform.
While there has been some debate about which programming language should be used behind the scenes at Twitter, RoR continues to support a significant portion of operations. For example, the Twitter Web interface still uses the Ruby on Rails framework.
AirBnB
AirBnB is a very popular and relatively complex web app developed using Ruby on Rails. Because AirBnB is designed to allow users to rent and lease various accommodation types, the app needed to be flexible and scalable.
RoR provides this, meaning new features can be added as necessary as AirBnB expands.
Shopify
Shopify provides a platform for users to build and run their own online store. Thanks to the versatility of RoR, Shopify allows users to perform a diverse range of tasks, including managing orders, taking payments, and customizing their storefronts.
3 reasons why Ruby on Rails isn’t usually used for ML
Machine learning has a range of benefits for companies, so it’s no surprise that it’s seeing increased adoption.
While Ruby on Rails has many positive features, it also has a few potential drawbacks which could cause developers to search for alternatives.
Myths about Ruby on Rails’ speed
Many developers maintain that Ruby on Rails lacks set-up speed. This assumption is often based on the fact that RoR is a comparatively old framework, so many view it as outdated.
This, however, is incorrect. Updates have been regularly made to Ruby on Rails since its inception, with performance a key concern of those working on it behind the scenes.
While the boot speed and runtime speed are comparatively slower than other contemporary environments – as shown by results from performance monitoring tools – RoR has several features that can increase development speed, helping to mitigate this.
Lack of libraries
Development using RoR is heavily dependent on gems, which are libraries that allow users to add functionalities without writing code. Unfortunately, documentation for many of these gems is lackluster, especially for lesser-known libraries.
For this reason, developers may have trouble understanding libraries and how they are utilized without testing them first to understand how they perform. This can slow down development, especially if implementing a functional testing plan.
Couple this with the fact that the choice of machine learning libraries for Ruby on Rails is somewhat lacking in the first place, and it’s understandable why developers may search out alternative platforms.
Decision tree
Ruby on Rails was not designed with machine learning and artificial intelligence applications in mind. This means that there is a lack of libraries that would enable a decision tree functionality.
A decision tree is a supervised learning approach used to extract value from data sets. Decision trees are used as predictive models in order to draw conclusions, continually splitting datasets into subsets based on a variety of variables and classifications.
Decision trees are one of the most commonly used machine learning algorithms. Therefore, because RoR is not designed with decision tree support in mind, its applications in machine learning are somewhat diminished.
You can get around this, though, by accessing the necessary gems for building a decision tree.
Ruby on Rails isn't going away anytime soon
Some may consider Ruby on Rails to be an outdated tool for web development. But the truth is that it has many positive aspects that will likely keep it in use for many years to come.
The large RoR community combined with its associated support network and gem libraries mean that Ruby on Rails has numerous applications across several industries. If there’s something you want to accomplish with RoR, chances are there’s already a solution for it.
The use of gems makes development with Ruby on Rails quick and easy. This is highlighted by the sheer number of successful startups using Ruby on Rails with great success.
Testing is built into Ruby on Rails from its inception, which helps to reduce development time, whether you’re using automated testing or manual testing material.
While it may not be the best-suited programming language for machine learning at first glance, the pros of Ruby on Rails far outweigh the cons.
Emily Rollwitz - Content Marketing Executive, Global App Testing
Emily Rollwitz is a Content Marketing Executive at Global App Testing, a software testing company helping top app teams deliver high-quality software anywhere in the world. She has
5 years of experience as a marketer, spearheading lead
generation campaigns and events that propel top-notch brand performance. Handling marketing of various brands, Emily has also developed a great pulse in creating fresh and engaging content. She’s written for great websites like Airdroid and SME News. You can find her on LinkedIn.
