A Systematic Review of MLOps Tools: Tool Adoption, Lifecycle Coverage, and Critical Insights

Machine learning operations (MLOps) has become increasingly critical as more organisations deploy machine learning models into production. However, the growing landscape of MLOps solutions has introduced complexity for practitioners trying to select appropriate tools. To investigate how and why these tools are adopted in practice, this paper conducts a systematic review of academic literature focused on MLOps tools. This analysis maps tools to MLOps lifecycle components to reveal their function, scope, and the challenges they are designed to address. It identifies usage trends and synthesises reported benefits and limitations based on practical implementations. The most commonly used components, according to the findings, are orchestration frameworks, data versioning, experiment tracking, and managed cloud platforms. No single tool covers the entire lifecycle, so researchers often combine multiple tools to build complete pipelines. This highlights the importance of interoperability across MLOps tools in real-world MLOps pipelines.