Your company is developing an MLOps platform to automate machine learning experiments and manage model retraining workflows efficiently. With dozens of ML pipelines to handle, it's crucial to properly organize and store the different artifacts produced. Considering scalability, integration, and management of metadata, how should you store the pipelines' artifacts?