A retail company's data science team has built several ML models, but they repeatedly run into problems: features computed during training differ from those used at prediction time, and different teams re-engineer the same features independently. They want a managed Google Cloud capability to centrally store, share, and serve consistent feature values for both training and online prediction. Which solution should they adopt?