You are building an ML pipeline to process and analyze both streaming and batch datasets. The pipeline must handle data validation, preprocessing, model training, and model deployment in a consistent and automated way. You need to design an efficient and scalable solution that captures model training metadata and is easily reproducible. You also want to be able to reuse custom components for different parts of your pipeline. What should you do?