MLOps streamlines the machine learning lifecycle from data science to production, integrating model development, deployment, monitoring, and governance. It bridges data scientists, DevOps, and operations teams, enabling automation, standardization, and scalability. Key components include ML infrastructure (data ingestion, pipelines, monitoring, deployment), automation (AutoML, hyperparameter tuning, model selection, preprocessing), and workflow best practices. By automating repetitive tasks, optimizing resources, and ensuring compliance, MLOps accelerates time-to-market, improves model accuracy, reduces costs, and supports continuous improvement in AI-driven applications.

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