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 with all of the steps required, from containerization with Docker to purchasing an Amazon web server to host the model. What I thought would be a fast process took me several days of development and a lot of debugging.
If you’ve built a machine learning model that works well, you probably want to share it so that others can try it out. The Gradio library makes it really easy to create a shareable GUI & public link for your model, as long as the model is running on your local computer. But what if you need a long-term hosted solution? That’s what GradioHosted is for!
In this tutorial, I’m going to show you, step by step, how to create and deploy your machine learning model app on GradioHosted.
GradioHosted lets anyone use your model interactively at any time— even…
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 of the most basic machine learning tasks, often used as an example of a “solved problem” in introductory machine learning courses. Yet, even a machine learning classifier that achieves 99% accuracy can make incorrect predictions when digits are rotated in unusual ways:
The aim of this blog post is to…
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. But what if you need a long-term hosted solution?
In this tutorial, I’m going to show you, step by step, how to create and deploy your machine learning model and UI on an AWS EC2 instance. …
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 explicitly stated in the docstring of a function. Consider this example from the math module in Python (implemented in C):
The contract in the docstring has two parts:
Gradio is being used by some of the world’s leading companies and machine learning teams, from Stanford’s Human-Centered AI to Cisco AI. How do they use it, and how can Gradio accelerate your machine learning (ML) team’s workflow? In this tutorial, I give a brief introduction to Gradio and outline 3 business use cases where Gradio allows you to do weeks of engineering work in under an hour.
Intro to Gradio
Gradio is an open-source Python package that automatically creates fully-functioning web apps with GUIs around ML models, in just 3 lines of code. With Gradio’s interfaces, non-ML users can…
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: https://arcane-tundra-83748.herokuapp.com/ (might take a minute for the dyno to start up)
Have you ever trained a machine learning model that you’ve wanted to share with the world? Maybe set up a simple website where you (and your users) could try putting in their own inputs and seeing the models’ predictions? It’s easier than you might think!
In this tutorial, I’m going to show you how to train a machine learning model to recognize digits using the Tensorflow library, and then create a web-based GUI to show predictions from that model. You (or your users) will be able to draw arbitrary digits into a browser, and see real-time predictions, just like below…
If you’re thinking about going for a PhD, there are lots of excellent guides I would recommend reading. However, most guides I’ve read focus on the high-level picture of pursuing a doctorate, not how it feels day-to-day to be sitting in your lab, doing research.
When I started my PhD at Stanford three years ago, I had no idea how much time I would be banging my head against a difficult problem and staring into a monitor with no apparent solution in sight. I also could not have realized how amazing it would feel when an unexpected epiphany hit me…