Uber's Michelangelo vs. Netflix's Metaflow

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  Uber's Michelangelo vs. Netflix's Metaflow Michelangelo Pain point Without michelangelo, each team at uber that uses ML (that’s all of them - every interaction with the ride or eats app involves ML) would need to build their own data pipelines, feature stores, training clusters, model storage, etc.  It would take each team copious amounts of time to maintain and improve their systems, and common patterns/best practices would be hard to learn.  In addition, the highest priority use cases (business critical, e.g. rider/driver matching) would themselves need to ensure they have enough compute/storage/engineering resources to operate (outages, scale peaks, etc.), which would results in organizational complexity and constant prioritization battles between managers/directors/etc. Solution Michelangelo provides a single platform that makes the most common and most business critical ML use cases simple and intuitive for builders to use, while still allowing self-serve extensibi...
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Science of motivation

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I worked in door-to-door sales for a couple of years, where we were paid commission to sell services, and were constantly pushed to work harder, longer and faster. I've also been studying to work in IT, where I've been advised to take my time and work through classroom problems slowly, carefully and thoroughly to maximize the quality of final solutions. Though both experiences taught me essential skills that I'm deeply grateful to have learned, here's an argument for the latter.

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