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Lessons in Linear Algebra at Scale with Apache Spark

Lessons in Linear Algebra at Scale with Apache Spark

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If you enjoy Linear Algebra, Spark, and exceptionally bad puns, then this could be the talk for you! In this session, we will chronicle the adventures of developing a large-scale Spark system in Scala at Target to power a text-based similarity engine by using core Linear Algebra concepts. You will not hear about a shiny system and how awesome it is, but instead you will learn about everything that went wrong and all of the lessons that were learned along the way. We will cover concepts like Cosine Similarity, Spark’s Distributed Matrix APIs, the Breeze numerical processing library under the hood that powers these APIs, among other things. We will embark on this system development journey together to understand what it took from beginning to end to pull a performant and scalable similarity engine together. Linear Algebra is often the backbone of many prominent machine learning algorithms, and the goal is that from this session, you will gain a deeper understanding into what gotchas exist and what is needed to design, tune, and scale these types of systems.
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