How do you design a scalable time series forecasting system in production?

A scalable system uses data pipelines, feature stores, distributed training, and model serving infrastructure with monitoring.

Production time series systems require ingestion pipelines (Kafka, batch ETL), feature engineering layers, model training pipelines, and real-time serving APIs. Scalability is achieved using distributed computing (Spark, Ray), model versioning, and autoscaling inference services. Monitoring for drift and retraining loops is essential.