Sketching data and other magic tricks
Who is this presentation for?
- Machine learning practitioners, data scientists, data engineers, and developers
Sophie Watson and William Benton explore a way to answer interesting queries about truly massive datasets almost instantly and with a fixed amount of space.
It sounds like magic, but you’ll go hands-on to practice sketching data structures that work this magic and the key trick that makes them possible. Sophie and William introduce truly scalable techniques for several fundamental problems like set membership, set and document similarity, counting kinds of events, and counting distinct elements. You’ll learn how and when to use these structures as well as how they work. You’ll see how the same techniques work for parallel, distributed, and stream processing at scale. And you’ll leave able to put these techniques to work in real data engineering and machine learning applications like join processing, document classification, and content personalization.
- A working knowledge of Python
Materials or downloads needed in advance
- A WiFi-enabled laptop
What you'll learn
- Learn how data sketches summarize large amounts of data in constant space and time; what problems different data sketches are useful for, including the bloom filter, Count-Min Sketch, HyperLogLog counter, and MinHash signatures; how to use these techniques for distributed computing and stream processing; and applications of data sketching for database and ML applications
Sophie Watson is a software engineer in an Emerging Technology Group at Red Hat, where she applies her data science and statistics skills to solving business problems and informing next-generation infrastructure for intelligent application development. She has a background in mathematics and holds a PhD in Bayesian statistics, in which she developed algorithms to estimate intractable quantities quickly and accurately.
William Benton is a senior principal software engineer at Red Hat, where he leads a team of data scientists and engineers and has applied machine learning to problems ranging from forecasting cloud infrastructure costs to designing better cycling workouts. His current focus is investigating the best ways to build and deploy intelligent applications in cloud native environments, but he’s also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology.
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