What are the primary differences between traditional DevOps and MLOps?

WHAT THIS TESTS: The interviewer wants to know if you understand that MLOps is a superset of DevOps with fundamentally different artifacts and feedback loops. They care whether you recognize that machine learning systems are probabilistic, data-dependent, and require governance beyond source code. This question separates candidates who have merely deployed models from those who have operated them in production.

A GOOD ANSWER COVERS: First, artifacts. In traditional DevOps the primary artifacts are source code, configuration files, and container images. In MLOps the artifact list expands to model binaries or serialized model files, training datasets, validation datasets, feature engineering pipelines, feature store definitions, experiment metadata, and data schemas. Second, triggers. DevOps deployments are typically triggered by code commits, pull requests, or dependency updates. MLOps deployments add triggers like data drift detection, scheduled retraining windows, model performance decay alerts, concept drift signals, and new data availability. Third, governance. A senior answer mentions that MLOps requires model registries, data lineage tracking, and validation gates such as model evaluation against a holdout set or A-B testing before promotion.

COMMON WRONG ANSWERS: The biggest red flag is saying MLOps is just DevOps with a different job name or claiming the only new tool is a GPU. Another mistake is ignoring data entirely and treating a model file like a static binary that never changes. Candidates who say deployments only trigger on git commits reveal they have not built automated retraining pipelines. Equally bad is conflating model training with model deployment; the interviewer wants to hear that training and serving are separate stages with different cadences.

LIKELY FOLLOW-UPS: Expect the interviewer to ask how you handle rollback when a deployed model degrades in production. They may ask about shadow deployments or canary releases for model updates. Another common follow-up is how you version datasets alongside code, or how you monitor for data skew between training and inference. Be ready to discuss immutable model artifacts and why you should never mutate a deployed model in place.

ONE CONCRETE EXAMPLE: Imagine an e-commerce recommendation system. In DevOps, a deployment triggers when an engineer merges a pull request changing the API logic. The pipeline builds a container image and deploys it. In MLOps, the pipeline also triggers when weekly user behavior data shows a drift in click-through distributions. The system retrains the collaborative filtering model on the new dataset, produces a new model artifact, runs offline evaluation against last week's holdout set, registers the new version in a model registry, and promotes it to a canary deployment measuring conversion lift before full rollout.