We will build a personalized recommender service that predicts the best venue for a drink for you based on your preferences (busy or less busy) and real-time Foot traffic Data.

We will then sprinkle some AI magic dust on the service by enabling you to ask the service questions, like - "can you recommend a bar within 15 minutes walking distance"?, using a LLM that has been fine-tuned to support function calling.

This tutorial may sound daunting, but we will only be really building 3 programs and a UI. We will use only Python, free serverless ML infrastructure, provided Hopsworks, and an API providing real-time footfall data.

Your ML system will consist of 3 ML pipelines:

• a feature pipeline to create the features for training and inference
• a training pipeline to take features and create a personalized ranking model
• an inference pipeline to find candidate bars and rank them based on your preferences

We will then build an open-source LLM-powered UI in Python so that you can interact with your system in natural language. The LLM will use the function calling paradigm to query the structured data in Hopsworks Feature Store.

We will write all our programs in Python and use free serverless ML infrastructure, provided Hopsworks, and an open-source LLM.

After all that hard work, you’ll know the best place for you to go and get a beer.

Register here

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