With the latest technological advancements, Machine Learning is becoming increasingly popular. This form of Artificial Intelligence (AI) enables computers to learn new things autonomously, rather than being explicitly programmed. The success of web applications and services such as Netflix, Amazon, Facebook, Spotify, etc. is indicative of the rapid growth in the market.
So, when attempting to develop a startup and create an output like this, which is the better option: AI with Ruby on Rails, or Machine Learning with Ruby on Rails? Before exploring the topic further, let's quickly review what Ruby on Rails development entails.
What is Ruby on Rails?
Ruby on Rails (or ROR) is an open-source framework powerful enough to create web applications. It provides an efficient platform for managing websites and databases, making it one of the best frameworks for reducing development time spent on scripts. This is why more businesses are searching for experienced Ruby on Rails development services for their web development projects.
What is Machine Learning?
Perhaps the most well-known definition of Machine Learning is that it is "the branch of computer science that enables computers to learn without explicit programming." Put simply, data science includes machine learning which is used to program computers to make predictions about future events using various large and connected data sources. It is a powerful technique to identify any kind of uncertainty, such as recommendations, forecasts, or detections of already-reported scenarios.
With Machine Learning, a computer can learn on its own what to do. To make your applications clever and forecast uncertain values, though, you will need to use specific methods and structures, such as neural networks. This is because the computer can develop the capacity to be intelligent and learn new things, meaning algorithms no longer need to be explicitly programmed.
How Does It Work?
With machine learning, a computer may learn on its own what to do. However, if you want your applications to be clever and forecast uncertain values, employ specific structures and methods, such as neural networks.
These technological advancements mimic how the human brain works when learning new information and making predictions. We find a clear analogy of how it might function in the fact that neural nets, for instance, are made up of layers of units called neurons.
Is Ruby on Rails the Perfect Platform for Machine Learning?
Neural networks, for example, are composed of layers of units called neurons and this technology mimics how the human brain works when learning something new and making predictions.
So, is Ruby on Rails the perfect platform for Machine Learning? Ruby is a programming language commonly used for scripting and web development tasks. When using Ruby on Rails development services, it is much quicker and more reliable to create Minimum Viable Products (MVP). Gems, which are like software packages, can be used for this and can quickly and easily solve a wide range of problems.
Task automation: Task automation is one of the reasons why Ruby on Rails is so popular among developers, as this is a great advantage in the world of technology. With Rails predefining the routes, all you need to do is follow them to find the answers. By using routes automated by Rails, the Model-View-Controller (MVC) can easily be wired.
Uncompromising Architecture: Uncompromising architecture is another feature of Ruby on Rails, providing excellent web architecture for developers to work with
2x Faster: The use of pre-existing plugins, modular designs, and a lighter code base can speed up Return on Investment (ROI) by up to two times when creating new web services and applications. This framework is reliable and suitable for startups who wish to develop their own independent web service portal.
Stack of Rail Libraries: Rails libraries have long helped developers access the precise data they need to build web apps. One of the most frequently suggested libraries for developing compelling and interactive web services.
RoR as a Web Application for ML: Rails libraries have long been helping developers access the exact data required to build web apps.
However, it does not ensure optimum performance in terms of the accuracy of difficult and time-consuming computations.
Yet, for accurate and complex computations, Ruby on Rails does not guarantee optimal performance. Therefore, it is preferable to combine Python (for microservices executing machine learning calculations) with Ruby on Rails' outstanding architecture. This combination provides the strongest combination of computing power and stability for web application development, also reducing the amount of time needed to construct a prototype, plus providing a high level of usability.
Conclusion
To conclude, it is advisable to combine several technologies and select the best tools to support them in Machine Learning ecosystems rather than relying solely on one sometimes-suboptimal option. As a leading provider of Ruby on Rails development services, our experienced team provides you with practical technical solutions to expand your business. Our commitment to fast technical solutions and quality drives us to offer the best services, setting us apart from other companies.
