Forecasting at Scale

Ray is a unified framework that allows organizations to effortlessly scale their Python and AI workloads, providing an open and portable toolkit for the entire ML lifecycle and python native applications. Nixtla offers a set of libraries intended to make available comprehensive and performant forecasting capabilities in a simple and easy-to-use Python library. Nixtla's efficient algorithms leverage the computational scalability of Ray to enable easy forecasting at scale, with recent demonstrations showing the ability to train 1 million models in under 30 minutes. Anyscale provides a scalable infrastructure for time series modeling, allowing organizations to quickly allocate and downscale resources based on their forecasting cycles, providing accurate results in a timely and cost-effective manner. Together, Ray and Nixtla provide a powerful open-source solution for organizations looking to perform forecasting and anomaly detection at scale, with the potential to reduce errors by 20-50% and losses in sales and product unavailability by up to 65%.

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