Gabriel Stechschulte (@gstechschulte.bsky.social)

This is a really cool blog showing how Uber is using Ray to speed up the optimizing component (Alternating Direction Method of Multipliers) within their budget allocation system. www.uber.com/en-CH/blog/h...

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How Uber Uses Ray® to Optimize the Rides Business Computational efficiency is a significant challenge when scaling solutions to a marketplace as large and as complex as Uber. The running and tuning of the Uber rides business relies on substantial numbers of machine learning models and optimization algorithms. Luckily, many pieces of the system can be treated in parallel. Ray® is a general compute engine for Python® that’s designed for ML, AI, and other algorithmic workloads. We describe how Uber has adopted Ray to enable mission-critical systems. Using Uber’s mobility marketplace allocation tuning system as an example, we found performance improvements of up to 40 times that unlocked new capabilities. It also improved developer productivity by increasing iteration speed, reducing incident mitigation time, and lowering code complexity. https://www.uber.com/en-CH/blog/how-uber-uses-ray-to-optimize-the-rides-business/