API World 2023 + AI DevWorld 2023

Fabio Buso, Hopsworks, VP of Engineering

Only 4 years ago, if you wanted to build a production ML system, you would need tens of engineers to build and maintain a ML platform, such as Michelangelo at Uber. Now, with serverless ML platforms, you can build an operate a ML system in minimal time.

In this tutorial, we will build a serverless ML system from three different Python programs that, when plugged together make up a production ML system.
The programs are
* a feature pipeline that we will schedule to run at an interval using Modal;
* a training pipeline that we run on demand (Colab)
* a batch inference pipeline that produces predictions that we show on a Dashboard (Gradio/HuggingFace)

For this tutorial, you will need experience with programming in Python, a laptop and Internet access.