Call it Ruby On Rails or simply Rails/RoR, it’s a world-known server-side web application development framework supporting MVC and is best known for web services and web page development. Besides this, using Ruby on rails for mobile app development makes a great aid for crafting Android or iOS apps.
No wonder why companies like Airbnb, Twitter, and Shopify are using RoR on Ruby On Rails. It’s the developers’ top choice because it leads to quick app maintenance because of the use of stuff migration. Updating the app and adding new functionalities is easy if apps are built on this Ruby on Rail.
But is it a good choice to make when you’re doing machine learning development? Can you use it as a machine-learning language? If you also want to explore the use of RoR as a machine-learning language, this post will be of great help.
Ruby On Rails As A Machine Learning Language – Is It Possible?
Machine Learning, or ML, is a data science branch enabling computers to learn without being dependent on a specific program or software. Often, it involves using computers that can predict the results of certain actions according to the pre-provided information.
Now, the main concern here is:
Can RoR be used in machine learning development or as a machine learning language?
Well, the answer is Yes. As we all know, RoR development doesn’t force Ruby on rails for mobile app development or web development experts to be involved in more choices as development proceeds. It can steer the development in the right direction. New developers can easily play with this framework.
As you use RoR as a machine learning development, here are the perks you’re eligible to enjoy:
- More flexibility and adaptability
RoR is one of those few frameworks that keep on offering frequent updates. Hence, developers enjoy better flexibility. Without making much effort, you can add updated functionalities to your app.
Machine learning strives for frequent updates and development so that new information can be added to the source. RoR can help developers to get rid of any particular element at any stage. With RoR, it’s possible to have hold of highly updated, flexible, and easy-to-adapt machine learning.
- Better configuration management
The best development practice is to keep codes and configurations separate. But, this isn’t the case with ML, as cloud configurations and codes are intertwined. However, the use of Ruby on Rails can make things better as it pre-defines the testing, development, and production ecosystem and allows developers to run different configurations as per the objectives.
- Quick pipeline composition
While you’re dealing with machine learning, training-serving skew is a very common issue to deal with. Using RoR can fix this issue as it permits developers to convert the existing pipeline into a serving pipeline for both the online processing and batch.
- Hassle-free dependency management
RoR comes with a fantastic tool, Bundler. It records dependency versions as you set up the development ecosystem. This makes a huge difference, as ML development is only successful when decencies are as less as possible and are effectively managed. Gladly, Bundler is here for the job.
- More interaction during development
When you hire Ruby on rails developer, you get to enjoy a highly interactive console that enables developers to test codes without actually touching the final product. This way, testing becomes a continual and result-driven process that leads to better and more responsive machine learning development.
ML Development Challenges with RoR
While you use RoR as machine learning, you need to make sure that this framework is not flawless. There are certain loopholes. For instance:
- You might find its libraries very restricted
A major part of RoR-based development is based on gems that are libraries allowing developers to add functionalities in the development process with the least possible code writing. But, RoR offers limited libraries, and even those offered are very lackluster.
These limited libraries have a direct impact on the development as it becomes slow and testing becomes tedious.
- Lack of decision tree
When we talk about machine learning development, decision trees are very important. ML uses decision trees as key algorithms. Sadly, RoR doesn’t have decision trees by default. Even though you can build decision trees with the help of gems, your capabilities will be limited.
Ruby On Rails is a great web and mobile app development framework to try out. You won’t be disappointed as it’s flexible, has great community support, and makes app maintenance & update easier than ever.
As you plan to use this advanced framework for machine learning, things are easier than ever. But the lack of gems and decision trees is something that you have to deal with excessively. Hence, the wise step to take here is to hire Ruby on Rails developers that are skilled and can use the strengths of this framework and fix the issues.
Author Bio :-
Chandresh Patel is a CEO, Agile coach and founder of Bacancy Technology – Ruby on Rails Development Company. His truly entrepreneurial spirit, skillful expertise and extensive knowledge in the Agile software development services has helped the organization to achieve new heights of success. Chandresh is fronting the organization into global markets in a systematic, innovative and collaborative way to fulfill custom software development.