And the Debate still exists, Ruby on Rails in Machine Learning and Artificial Intelligence: Whether or Not
Technology is becoming advanced, and with the latest development in technology, Machine Learning is on the rise. As we know, machine learning is nothing but a type of artificial intelligence that makes the computer self-sufficient to learn new things without being explicitly programmed.
The rapidly growing market has resulted in successful web applications and services such as Netflix, Amazon, Facebook, Spotify, etc. So, the question is when you are looking for such kind of output and development to enhance your startup, is AI with Ruby on Rails or Machine Learning with Ruby on Rails a perfect choice?
How would you better define Machine Learning?
Machine learning and data science are the two sides of the same coin. You can’t work on one of them by avoiding the other. The machine learning automatically behaves by predicting the unknown results depending upon the related data sources. The uncertainty is almost diminished as the predictions are based on the behavioral actions of the users, and hence, the recommendations are also correctly displayed.
When the computer itself gains the ability to smartly learn the mindset of the person using the application or web solution, it is called as AI learning or machine learning in a layman’s language.
What is Ruby on Rails?
Like any other web application framework, ROR is also a server-side web application framework written under the MIT license for developing robust web applications. When you are looking to develop a dynamic web service or application, Ruby on Rails work is generally the first choice of web service developers.
Also Read: Why Use Ruby on Rails?.
Automation of tasks: Ruby on Rails has been a preferred choice for the developers because it assists with task automation, which is a blessing in the technology world. All routes are set up the Rails itself; it means you need just to follow the rails to get all the clues and tips. The MVC is easily stringed with the help of routes driven by the Rails through automation.
Uncompromising Architecture: ROR is the best example of uncompromising architecture in developing the web application framework. When you choose ROR, you get the best web architecture to work with. It is just awesome to get along with it for developing new web services and applications.
2x Faster: The leaner code-base, as well as the use of existing plugins along with modular designs, turn ROI as 2X faster for developing new web service frameworks such as apps and services. You can trust and recommend the framework for startups, who are looking to design their independent web service portal.
Stack of Rail Libraries: Rails libraries have always been supportive of the developers in getting the exact information while creating web applications. It is one of the most recommended libraries for creating interactive and engaging web services.
The famous Ruby on Rails Web Services used worldwide are: Basecamp, Kickstarter, Airbnb, GitHub, ASKfm, Couchsurfing, SlideShare, etc. to name a few. These are all successful web application frameworks that are used by millions of people worldwide.
So, is Ruby on Rails recommended for Machine Learning or AI?
Machine Learning: Ruby vs. Python
As with the help of the ROR framework, the developers can create MVPs in a better and faster way; the framework is highly recommended for creating beautiful web frameworks and services. But when it comes to Machine Learning with Ruby on Rails is not justified enough.
The best alternative for the same is ‘Python’.
What is Python?
Python is a powerful yet fast programming language that is both user-friendly and quick to learn for the Machine Learning engineers. It is an open-source platform for creating super-interactive website frameworks, including AI-based web applications, and has become so much popular in so little time.
Why is Python the right choice for Machine Learning?
- Python has always been the recommended web framework for data sciences as it has excellent numbers of packages to offer for the relevant field. The best examples of the same are TensorFlow, pandas, Keras, and numpy. The entire new project set-up can be done quickly using these computations. Above all, the time needed for the same is quite less compared to other frameworks.
- The above mentioned and many more libraries are simple yet powerful. So, in complex development, it’s only fast and efficient but stable as well. In the long run, these tools are mature and reliable.
- Another significant benefit of using the Python library is huge support from the Python community, and this what makes so convenient to find support tutorials for the development process. So, without a doubt, you can rely upon these new technologies when it comes to building anything from scratch.
- Python is a general-purpose, interpreted, and high-level OOPs based dynamic programming language that focuses on rapid application development and doesn’t repeat yourself. Due to ease of syntax in Python, the programmers can complete coding in fewer steps as compared to Java or C++. Python is considerably one of the fastest-growing languages. Python’s ever-evolving libraries and support make it a viable choice for any project, be it Mobile App, Web App, IoT, Data Science, or AI.
The USP of using python for a new data science project is:
- It’s Flexible
- It’s easy to learn
- It’s open-source
- It’s well-supported
If you are interested in knowing about what other possibilities can be explored using Python, then get in touch with Python development expert.
Choosing the best python library for machine learning and AI is essential. According to our research and experience, we recommend using Tensorflow for the Python-based AI project developments. TensorFlow is an easy model building framework for developing AI-based frameworks. It has got powerful experimentation to deliver desired results.
Which Programming Language is Best for Machine Learning?
Like Microsoft’s DMTK, Google TensorFlow is an automatic learning framework designed to scale across multiple nodes. As with Google’s Kubernetes, it was built to solve problems internally in Google, and Google has finally chosen it to launch it as an open-source product. TensorFlow implements data flow diagrams, where data batches (“tensors”) can be processed by a series of algorithms that are described by a graph. The movements of the data through the system are called “flows” – hence the name. Graphics can be assembled with C ++ or Python and can be processed in CPUs or GPUs. Google’s long-term plan is for TensorFlow to be developed by third-party contributions.
Choosing TensorFlow is Highly Advisable:
- It delivers excellent documentation
- Free of heavy operations
- Better performance in terms of graphics
- The building of neural nets
- Unique computational engine
Native developers are going crazy in the discussion of Ruby vs. Python and Python over Ruby because it is one of the most preferred and convenient programming languages for developing data sciences, machine learning, and AI-based solutions. Python is offering a far more convenient then Ruby machine learning gem.
At Bacancy Technology we are globally renowned Python developers, and we have proven expertise in building Python based Machine Learning and Artificial Intelligence modules. If you are still wondering why you should opt Python and not Ruby on Rails for Machine Learning, then get in touch with our expert to discuss in detail.
Ruby on Rails in Machine Learning: FAQs:
Why Use Python for AI and Machine Learning?
AI based projects are completely different from traditional software developments. The significant difference lies in the tech stack, as AI-based projects require deep research. To implement your AI aspirations, a stable and flexible language like Python makes great sense.
How can I practice Machine Learning with RoR?
If you search on Github for “Machine Learning Ruby,” you will come across numbers of Ruby-based ML tools. Yes, Ruby is not an ideal choice for machine learning processes, but Ruby is actually a good choice for user interaction and API functions. However, Java works well for the classification tasks and actual training as it works perfectly well.
What does a Full Stack Developer do?If Ruby does not match Machine Learning, then what’s the alternative?
It’s Python. Python is a general-purpose, interpreted, and high-level OOPs based dynamic programming language that has always been the recommended web framework for data sciences as it has excellent numbers of packages to offer such as TensorFlow, pandas, Keras, and NumPy. The whole new project set-up can be done quickly using these computations.