Your data science team is working on several machine learning experiments and needs to rapidly iterate with various features, model architectures, and hyperparameters. Tracking and reporting the accuracy metrics of these experiments efficiently is crucial. They also need an API to query these metrics over time to analyze the performance of different configurations. What should they use to ensure smooth tracking and reporting of their experiments while minimizing manual effort?