The video below, “Thinking Sparse and Dense,” is the presentation by Paco Nathan from live@Manning Developer Productivity Conference, June 15, 2021. In a Post-Moore’s Law world, how do data science and data engineering need to change? This talk presents design patterns for idiomatic programming in Python so that hardware can optimize machine learning workflows. You’ll hear about ways of handling data that are either “sparse” or “dense” depending on the stage of ML workflow – plus, how to leverage profiling tools in Python to understand how to take advantage of the hardware. The talk also considers four key abstractions which are outside of most programming languages, but vital in data science work.
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