What should an ML framework that optimizes for user happiness look like?

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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.

I wonder if the same type of discussions happened during the early days of the internet when web development frameworks appeared. My first encounter with web development was when I learned about the LAMP stack. Out of curiosity, I learned the basics of PHP, JavaScript, HTML, and CSS, but I found it too difficult to stitch them together to build a website. There were just too many ways of doing the same thing, and most of them seemed incorrect. At that point, I thought: one must learn a lot even to build a simple website. Then, I learned about web frameworks while taking an online course on software engineering: it was such a delightful experience!

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

Source: towardsdatascience.com