Rails allows anyone to build a blog engine in 15 minutes; how would this translate to the Machine Learning development world? This post represents my vision of what a Ruby on Rails for Machine Learning should look like.
I’ve been talking to many data practitioners these past few months, from Ploomber users to maintainers of other data tools. A recurrent topic has been the state of the Machine Learning tooling; discussions often revolve around the proper API for a Machine Learning framework.
The course showed how to develop a software application using Ruby on Rails. I didn’t know much about web technologies, but the framework made it much easier for me: it reduced the number of decisions I had to make and provided a consistent path to get things done. I tried a few other frameworks, such as Django, but none matched Rails’ development experience; it’s no surprise that the first pillar of The Rails Doctrine is Optimize for programmer happiness.
Fast forward, I started working on Machine Learning projects and felt like I was on the inefficient route again. There were many ways of achieving the same thing, with most of them feeling incorrect.
We refer to projects such as scikit-learn, PyTorch, or Tensorflow as Machine Learning frameworks. Still, they are Machine Learning training frameworks because doing Machine Learning is much more than training a model; this is equivalent to calling ORM frameworks, web frameworks.
Web frameworks allowed individuals to develop web applications quickly, but we currently need a whole team to build and deploy Machine Learning models. I’m sure that at this point, you’ve heard the 87% Machine Learning projects fail statistic more than a dozen times.
Simplifying the development of Machine…
Continue reading: https://towardsdatascience.com/we-need-a-ruby-on-rails-for-machine-learning-626373dbd210?source=rss—-7f60cf5620c9—4