Design an end-to-end training framework
Overview
This question evaluates the ability to design production-grade end-to-end training and serving frameworks for time-series forecasting, assessing competencies in ML system architecture, data and feature engineering, reproducibility and configuration management, model lifecycle/versioning, training loop and experiment management, and operational concerns like monitoring, inference pipelines, and CI/CD. It is commonly asked in ML system design interviews to probe practical application of scalable PyTorch-based workflows and engineering trade-offs; it belongs to the ML system design domain and primarily assesses practical application with system-level and architectural abstraction rather than purely conceptual algorithmic knowledge.
Design an End-to-End Time-Series Forecasting Framework (PyTorch)
You are tasked with designing a production-grade, end-to-end framework for training and serving time-series forecasting models using PyTorch (or a similar deep learning library). Assume a multi-project, multi-model environment where teams reuse common infrastructure.
Make minimal platform assumptions: Python 3.x, PyTorch 2.x, containerized workloads on Linux, object storage for artifacts, a message bus for streaming, a metrics backend for monitoring, and a simple model registry. Specify clear component boundaries and interfaces to enable team collaboration and CI/CD.
Requirements
-
Data ingestion and dataset abstractions
- Sliding/windowed sampling and multi-horizon forecasting support.
- Online/offline feature generation with parity.
- Handling multiple time-series (per-entity), calendar effects, and covariates.
-
Configuration management and reproducibility
- Centralized config, seed control, deterministic flags.
- Environment isolation and dependency pinning.
-
Training loop
- Mixed precision (AMP), gradient clipping, early stopping.
- Checkpointing and resume support (including RNG states).
-
Hyperparameter tuning and experiment tracking
- Define a search space and scheduler.
- Track metrics, artifacts, and plots.
-
Model registry and versioning
- Versioning and promotion gates (dev → staging → prod).
- Model signature/schema and metadata.
-
Inference pipelines
- Batch and streaming inference with defined latency/throughput SLOs.
- Feature parity between train and serve.
-
Monitoring, alerting, and automated retraining
- Drift and quality monitoring; alerting; retraining triggers.
-
Testing, CI/CD, rollback
- Unit, integration, end-to-end tests.
- CI/CD and rollback strategy.
-
Deliverables
- High-level module diagram description (textual OK).
- Key interfaces between components (method signatures/abstractions).
Solution
Login required