Compared to the Vertex AI flagship deep learning forecasting model (Learn2Learn), the TiDE architecture provides a 10x training throughput improvement (and in some cases up to 25x). The prediction throughput is also improved substantially, ranging from 3x to 10x in common tasks. This upgrade allows completion of most training jobs in just a few hours. Because of this reduced training time, in many cases migrating to TiDE can lead to significant cost savings.
TiDE is already helping Hitachi Energy advance the world’s energy systems to be more sustainable, flexible and secure. According to Bret Toplyn, Director of Product Management at Hitachi Energy: “TiDE presented exciting results for Hitachi Energy’s research in energy predictions using machine learning. What five teams took weeks to deliver, TiDE generated in mere hours with the same or better accuracy. The algorithm delivers compelling innovations in data science. Hitachi Energy plans to leverage TiDE to continuously improve its algorithms to produce better prediction results faster.”
Another review, TiDE: Revolutionizing Long-Term Time Series Forecasting by Philippe Dagher, summarized these research advances: “TiDE’s breakthrough is not just in its performance metrics, though they are undeniably impressive. It is in the underlying philosophy that simpler models, when designed with care and understanding, can not only compete with but even surpass their more complex counterparts.”
New backend for improved efficiency, transparency, and performance
TiDE is only one of the dozen improvements enabled by a new service backend, which now uses Vertex AI Pipelines to offer improvements like more transparency, built-in scheduling, support for larger datasets, customizable hardware, optional architecture search, and much more.
These improvements have already helped some of the top retail brands all around the world. Shriman Tiwari, Chief Data Scientist at Groupe Casino said, “Groupe Casino found a perfect partner in Vertex AI for demand forecasting across its expanding portfolio of over 450 hyper market stores. We were able to develop highly accurate, location and product specific forecasting models and saw a 30% improvement in forecast accuracy, and 4x reduction in model training and experimentation time.”
Tiwari also highlighted how better forecasts directly impacted Groupe Casino’s business and customers, noting, “Forecasting with Vertex AI helped in optimizing the inventory planning and reducing perishable goods wastage to increase revenue. For Casino’s clients, an improved forecasting led to a visible reduction of missing products leading to an increase in customer shopping experience.”
Built on Vertex AI Pipelines for more transparency and customization
Vertex AI forecasting models are now offered as transparent pipeline templates in Vertex AI Pipelines, with the following features now automatically available to every forecasting user.
