Made With ML: Discover, build, and showcase machine learning projects

This is a short introduction to Made With ML, a useful resource for machine learning engineers looking to get ideas for projects to build, and for those looking to share innovative portfolio projects once built.

Where do you turn when looking to discover existing machine learning projects, get ideas to build projects of your own, and then share these projects once they are complete?

There exist options for some portion of this discover/build/showcase pipeline of which you may already be aware, such as the Papers with Code project, which describes itself as:

Papers with code. Sorted by stars. Updated weekly.

Certainly a helpful resources, but if you are looking for something that much more balances the sharing of your own projects and the sense of community with the discovery aspect of Papers with Code, you should check out Made With ML.



Made With ML fills the role of a community which brings together those looking to get ideas and those looking to build and share a portfolio of projects, whether to provide others with ideas or to showcase what they have learned and built.

The main Made With ML page lists searchable projects like so:



Each project's page provides additional information, as seen below, including descriptions, code, blog posts, videos, etc., where relevant to a given project:



Made With ML's introductory blog post describes the project it the words of its founder, accomplished researcher, author, and founder Goku Mohandas.

So we worked with these hiring managers to create the ideal profile that would achieve two key tasks:

  • ???? Provide a portfolio to showcase projects that demonstrate technical and product sense.
  • ⏰ Do all of the above in about 2 minutes (average time a hiring manager spends on a resume).

And so we created Made With ML. It’s a platform to share and discover ML projects. You learn from other’s projects through discovery and search:

Read the blog post for additional info on the project, as well as some suggestions on the types of projects to share and showcase (hint: don't just throw a bunch of algorithms at a generic dataset; the emphasis should be on creating complete projects which perform a specific, useful task, and which would make others sit up and take notice).

Made With ML seems like it could fill a role somewhere between GitHub and Kaggle, and I encourage readers to have a look in case it may be helpful for them.