Real-World Inference you own this product

prerequisites
intermediate Python data science libraries • intermediate machine learning • intermediate recommender system experience (specifically Two Towers) • basic of developing an ML pipeline, Intermediate TensorFlow 2.x
skills learned
reconfigure a “notebook-only” recommendation system to a model capable of handling many-items per user • use linear-algebra to combine networks with differing dimensionality
Shaked Zychlinski
1 week · 4-6 hours per week · ADVANCED
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Look inside

Behind the scenes on websites like Amazon, Netflix, and Spotify, models make predictions on thousands of items every day. Then, based on what they’ve learned, they choose only the best recommendations to display for every individual user. In the real world, performing thousands of predictions one by one, as in a notebook-only model, would be highly inefficient. In this liveProject, you’ll reconfigure the models you implemented in the previous project to accept a list of items for each user and then evaluate all items at once—choosing the best recommendations much more quickly and efficiently.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Shaked Zychlinski

Shaked is currently leading the recommendation research group and company’s recommendations efforts at Lightricks, developing the company's RS algorithms from the ground up. Prior to this, he worked at and led projects at the Algo group of Taboola, one of the largest content recommendation companies in the world. He is a featured writer on Towards Data Science, with hundreds of reads each day. He has also developed the Dython library for Python, with 26k (and counting) downloads a month.

prerequisites

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:


TOOLS
  • Intermediate Python (NumPy, pandas, Matplotlib)
  • Intermediate scikit-learn
  • Basics of TensorFlow 2.x (Keras interface)
  • TensorFlow Recommenders (retrieval and ranking models)
TECHNIQUES
  • Basic linear algebra (vectors, spaces, matrix transformations)
  • Rank models using TensorFlow Recommenders

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