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.
Fabio Buso is VP of Engineering at Hopsworks, leading the Feature Store development team. Fabio holds a master’s degree in Cloud Computing and Services with a focus on data intensive applications
