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Discover how City Storage Systems has transformed its machine learning infrastructure by adopting Daft, seamlessly integrating it with our Ray-based platform. Previously managed on separate Spark clusters, our data processing and ETL tasks now leverage Daft’s intuitive DataFrame interface, which matches and even surpasses PySpark’s capabilities. This integration has streamlined operations across both individual and cluster environments, enabling a more unified and efficient workflow.
In this talk, we will showcase practical use cases from CloudKitchens where Daft has driven significant workflow improvements. Learn how this strategic shift to a consolidated Ray and Daft environment supports our end-to-end machine learning pipelines, from data engineering to model development.
In this talk, we will showcase practical use cases from CloudKitchens where Daft has driven significant workflow improvements. Learn how this strategic shift to a consolidated Ray and Daft environment supports our end-to-end machine learning pipelines, from data engineering to model development.
