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Production Machine Learning Systems Questions

Design, build, deploy, and operate end to end machine learning systems in production. Topics include data ingestion and validation, feature engineering and real time feature computation, training and testing pipelines, model serving and prediction latency optimization, scalability and reliability of infrastructure, and monitoring and observability for data and model performance. Covers detection and handling of data drift and model drift, retraining strategies and automation, versioning and reproducibility for data code and models, experiment tracking and model registries, and practices for continuous integration and continuous delivery in machine learning contexts. At senior and staff levels, expect system level trade offs, designing platform capabilities for multiple teams, debugging production performance regressions, and managing technical debt in machine learning systems.

HardTechnical
104 practiced
A senior engineer asks you to quantify technical debt in your ML pipeline. Propose a measurable framework (metrics and thresholds) to score technical debt items such as brittle feature engineering, lack of tests, opaque models, and manual steps in retraining. How would you prioritize remediation?
MediumTechnical
71 practiced
You have a batch scoring job that processes 1 TB of feature data daily to produce predictions. Latency is not strict, but cost is a concern. Describe ways to optimize for cost (compute, storage, I/O) and trade-offs you would consider.
EasyTechnical
61 practiced
A newly deployed model shows a sudden drop in precision on class A but overall accuracy is stable. Describe how you'd investigate the issue end-to-end and list the likely root causes you would check first.
HardTechnical
55 practiced
Design a monitoring and alerting strategy to detect model fairness regressions (e.g., disparate impact across demographic groups) over time in production. Which group-level metrics would you track, how to set thresholds, and how to investigate root causes when an alert fires?
MediumSystem Design
51 practiced
Design an automated retraining pipeline that uses MLOps primitives (data validation, feature engineering, hyperparameter search, training, validation, registry, deployment). Describe orchestration steps, failure handling, and how to ensure only quality models are promoted.

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