The text discusses the importance of forecasting in business, particularly when lead times are long and inventory management is crucial. It highlights the challenges faced by data scientists when training models for time series forecasting, especially when dealing with large datasets that require frequent updates to account for changing data distributions. The authors propose using Ray, an open-source library developed at UC Berkeley, to parallelize and distribute Python code for ARIMA and Prophet models, making it easier to scale up forecasting tasks without rewriting the underlying codebase. Anyscale is also introduced as a tool for managing and running Ray applications in the cloud, providing a multi-cloud strategy with no vendor lock-in. The authors demonstrate how to use Ray and Anyscale to distribute ARIMA and Prophet training and inference tasks, achieving significant speedups in runtime.