Don’t want to deal with Docker, Flask, and web hosting? Try this instead

Several years ago, when I built my first machine learning model to classify handwritten digits, I immediately wanted to show it off to my friends and my siblings so that they could see what I had built. I remember searching “how to deploy a machine learning model” and becoming frustrated…

A step-by-step guide on finding and understanding problems in your machine learning model — and fixing them!

Machine learning models can be used to build cool applications and demos. But it’s still a challenge to deploy machine learning systems reliably in the real world, because they often fail in unexpected ways when unexpected data comes around.

For example, classifying digits in the MNIST handwriting dataset is one…

You’ve built a demo for your machine learning model with Gradio. Now, deploy it with a permanent link on an AWS instance

If you’ve built a machine learning model that works well, you might want to demo it, so that others can try it out. The Gradio library makes it really easy to create a shareable GUI for your model, as long as the model is running on your local computer. …

Why pretrained machine learning models are often unusable and irreproducible — and what we can do about it


A useful approach to designing software is through contracts. For every function in your codebase, you start by writing its contract: clearly specifying what inputs are expected and valid for that function (the precondition), and what the function will do (the postcondition) when provided an appropriate input. This is often…

Gradio lets you build a UI for your machine learning model. Heroku lets you host it. Here’s how to use them together.

In this tutorial, I’m going to show you, step by step, how to create and deploy your machine learning model and UI on Heroku. I’ll use this drag-and-drop image interface that I created for the Inception Net model as an example:

You can try out the GUI at:

Abubakar Abid

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