Design an end-to-end training framework

Design an end-to-end training framework in PyTorch (or similar) for time-series forecasting. Specify components for: data ingestion and dataset abstractions with sliding/windowed sampling and feature generation; configuration management and reproducibility (seed control, deterministic flags, environment isolation); training loop with mixed precision, gradient clipping, early stopping, checkpointing, and resume support; hyperparameter tuning (search space, scheduler) and experiment tracking (metrics, artifacts, plots); model registry and versioning with promotion gates to staging/production; batch and streaming inference pipelines with latency/throughput SLOs and feature parity between train and serve; monitoring, alerting, and automated retraining triggers using drift/quality signals; testing strategy (unit, integration, end-to-end), CI/CD, and rollback plan. Provide a high-level module diagram description and define key interfaces between components.