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Spotify uses a range of Machine Learning models to power its music recommendation features including the Discover page and Radio. Due to the iterative nature of training these models they suffer from IO overhead of Hadoop and are a natural fit to the Spark programming paradigm. In this talk I will present both the right way as well as the wrong way to implement collaborative filtering models with Spark. Additionally, I will deep dive into how Matrix Factorization is implemented in the MLlib library.