WHY ML MODELS FAIL TO REACH PRODUCTION

Abstract

Machine Learning (ML) models are developed at an unprecedented scale, yet only a fraction ever reach production — and among those that do, many fail within months. This article examines the organizational, technical, and cultural factors that prevent ML models from being successfully operationalized, and presents structured practices to address them. Through a narrative bibliographic review drawing on peer-reviewed literature and technical documentation from recognized industry organizations, the article maps seven primary barriers to ML deployment: misalignment between data science and engineering teams, data quality and governance failures, lack of reproducibility, absence of robust testing, inadequate infrastructure, neglected monitoring, and organizational resistance. As a response to these challenges, MLOps — the convergence of Machine Learning, DevOps, and Data Engineering — emerges as a systematic framework for treating models as products rather than experiments. The article describes the full ML model lifecycle in production, surveys the current tooling ecosystem, and outlines practical strategies for building an MLOps culture incrementally, regardless of organizational size. Real-world cases, including Spotify's large-scale ML platform and lessons from the regulated financial sector, illustrate how structured operationalization practices translate into measurable business outcomes. The findings suggest that the primary determinant of ML success in production is not algorithmic sophistication, but the organizational and engineering discipline surrounding model deployment, monitoring, and continuous improvement.