Design a MLOps Pipeline

Problem statement

Design an MLOps platform that automates the entire ML lifecycle: data ingestion, feature engineering, training, offline validation, model packaging, deployment to test and production environments, and continuous monitoring for performance decay, data drift, and anomalies. The platform must make every promoted model reproducible, every deployment reversible, and every production behavior attributable to an exact combination of code, data, configuration, and environment.

This is not generic CI/CD with a model artifact swapped in. In classical software, the build is deterministic: the same source produces the same binary, and tests are written against fixed expectations. In ML, the compiled artifact depends on a moving input distribution. A pipeline can be fully green—code compiles, unit tests pass, training converges—while producing a model that is already stale because the training snapshot no longer matches serving traffic. Therefore the design must treat data as a first-class build input: versioned, hashed, validated, and pinned to every run, exactly like source code.

Why the problem is distinctive

Three properties separate MLOps from DevOps. First, there are two artifacts to integrate: code and data, plus a third derived artifact, the model. Continuous integration must therefore validate schema, statistics, and label quality before a single GPU spins up. Second, tests are probabilistic: a model can regress without any code change, so the gate between staging and production is an evaluation contract—metrics on held-out slices—not a boolean test suite. Third, deployment is not the end of verification; the model degrades in production as the world changes, so monitoring is part of the pipeline and can re-trigger training. The loop data → features → train → validate → deploy → monitor → retrain is continuous training, and the architecture must close that loop with human-approved or policy-approved gates.

The four architectural planes

1. Data plane: ingestion, validation, feature engineering, point-in-time correct datasets, and the online/offline feature store.
2. Training plane: orchestration of DAGs, hyperparameter search, distributed training, checkpointing, and experiment tracking.
3. Serving and release plane: model registry, packaging, environment promotion, canary and shadow deployment, automated rollback.
4. Governance and learning plane: lineage, drift and performance monitoring, retraining triggers, audit, model cards, and release evidence.

A strong interview answer keeps these planes separate, states which decisions are automated versus human-approved, and makes reproducibility and rollback architectural invariants rather than aspirational policies. The attached brief requires orchestration with hyperparameter search, a versioned registry, automatic deployment to test and prod, and monitoring for drift, performance, and anomalies; every number below that is not cited is an explicitly labeled design assumption.