Alan Tan

Companies need robust and cost-effective solutions to deploy foundation models (FMs) at scale. Additionally, SaaS providers need scalable and cost-effective ways to serve hundreds of models to their customers.

One of the major factors that can affect the accuracy of models is the difference between data used to generate predictions and data used for training. For example, changing economic conditions could drive new interest rates affecting home purchasing predictions.