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Real-world recommendation systems work with millions of users and billions of items. To handle this massive scale, recommendation systems tend to be divided into two types of models (retrieval and ranking), running one after the other, narrowing down the set of items each time. In this liveProject, you’ll implement a retrieval model using TensorFlow Recommenders, combine it with a ranking model, then create a fully functional pipeline going through both models, using a Feature Store. Finally, you’ll explore the scenario where you can’t (or choose not to) run both retrieval and ranking models online in real-time, leveraging the Feature Store once more to use the retrieval model offline.
This liveProject is for data scientists with theoretical knowledge of machine learning, deep learning, and recommender systems who want to take the next step in their career. To begin these liveProjects you will need to be familiar with the following:
In this liveProject, you’ll learn to create a fully functional pipeline going through both a retrieval and a ranking model, using a Feature Store:
geekle is based on a wordle clone.