How Anastasia accelerated their ML processes 9x with Ray and Anyscale

Juan Roberto Honorato is an AI Tech Lead at Anastasia.ai, where he helps develop an ML driven platform to deliver business solutions for its customers. The company's mission is to democratize AI for every business by providing scalable and cost-effective technologies. Recently, the team has been using Ray, a distributed computing framework, which led to astonishing results in demand prediction problems. Compared to their AWS Batch implementation, their Ray implementation was 9x faster and reduced costs by 87%. The blog post explores how Anastasia's platform works, their initial attempts at solving the problem using Python's built-in parallel modules, and how they realized the need for horizontal scaling with training and hyperparameter tuning. It also discusses how Ray helps them scale easily and how Anyscale further optimizes the system.

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