Instruction: Provide a clear definition of MLOps, and highlight the key differences between MLOps and traditional software operations practices.
Context: This question is designed to assess the candidate's foundational understanding of MLOps as a discipline and its unique aspects compared to traditional software development and operations. The response should cover the integration of machine learning models into the continuous integration/continuous deployment (CI/CD) pipeline, the importance of data versioning, and the specific challenges related to model monitoring and updating in production environments.
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The way I'd explain it in an interview is this: MLOps is the discipline of operationalizing machine learning systems across training, deployment, monitoring, governance, and improvement. It extends software operations by adding data dependencies, experimentation, model evaluation, drift, and the fact...