Your question is Distributed Foundation Model Training. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How would you design a distributed training pipeline for a foundation model using Ray or similar frameworks at Datadog?
Explain the end-to-end architecture, including data ingestion and preprocessing, distributed scheduling, GPU communication, checkpointing, experiment tracking, evaluation, and model artifact management. Discuss how you would size the system, choose between Ray Train, Ray Data, native PyTorch distributed, or alternatives, and handle failures, stragglers, preemption, data skew, feature drift, and training-serving skew. Address reproducibility, security, cost controls, and monitoring. State assumptions explicitly where scale, model size, or hardware requirements are not provided.