How would you design a data pipeline for training and serving AI models?

To design a data pipeline for AI models, I would first outline the stages involved: data ingestion, data preprocessing, feature engineering, model training, and serving. I would use tools like Apache Kafka for real-time data ingestion, and Apache Spark or TensorFlow Data for preprocessing and feature extraction. The pipeline should ensure data quality and consistency through validation checks. For model training, I'd implement distributed computing if needed, using frameworks like TensorFlow or PyTorch. After training, I would deploy the model using a service like TensorFlow Serving or Kubeflow, integrating monitoring tools to track performance metrics and data drift.