How do you ensure data privacy and security when deploying ML models in production?

Instruction: Explain the measures and practices you implement to safeguard data privacy and security in MLOps pipelines.

Context: This question gauges the candidate's awareness and application of data privacy and security measures in the deployment of ML models.

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I start with minimization: the model should only access the data it actually needs. Then I add access control, encryption in transit and at rest, secret management, audit logs, retention policies, and careful review of where sensitive features...

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How do you ensure data privacy and security when deploying ML models in production?

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