How do you design clustering systems with strict isolation between training and inference environments?

Isolation is achieved by separating feature pipelines, compute environments, and data paths for training and inference.

Training and inference must remain isolated to prevent data leakage and ensure stability. Training pipelines operate on historical datasets, while inference uses live feature stores. Differences in computation must be minimized via standardized feature definitions. Containerized environments ensure consistent dependencies but separated execution contexts prevent cross-contamination.