Introduction: Problem, Context & Outcome Machine learning initiatives frequently fail after the proof-of-concept stage. Teams build accurate models but struggle to deploy, monitor, and maintain them in production environments. Inconsistent data, missing automation, and weak collaboration between data scientists and DevOps engineers create repeated failures. As organizations increase their dependence on AI-driven systems, these challenges…

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