🤖 AI Summary
This study addresses the lack of systematic comparisons among mainstream MLOps tools, which hinders developers’ ability to select appropriate solutions for their specific needs. For the first time, it presents a multi-dimensional empirical evaluation of MLflow, Metaflow, Apache Airflow, and Kubeflow Pipelines under a unified experimental setup, using two representative tasks: MNIST image classification and IMDB sentiment analysis with BERT. The assessment spans six key criteria—installation ease, configuration flexibility, interoperability, code intrusiveness, result interpretability, and documentation quality—and incorporates a weighted scoring mechanism. By establishing a balanced evaluation framework that integrates both quantitative and qualitative insights, this work delivers a clear and practical guide for selecting MLOps tools tailored to diverse application scenarios.
📝 Abstract
Given the increasing adoption of AI solutions in professional environments, it is necessary for developers to be able to make informed decisions about the current tool landscape. This work empirically evaluates various MLOps (Machine Learning Operations) tools to facilitate the management of the ML model lifecycle: MLflow, Metaflow, Apache Airflow, and Kubeflow Pipelines. The tools are evaluated by assessing the criteria of Ease of installation, Configuration flexibility, Interoperability, Code instrumentation complexity, result interpretability, and Documentation when implementing two common ML scenarios: Digit classifier with MNIST and Sentiment classifier with IMDB and BERT. The evaluation is completed by providing weighted results that lead to practical conclusions on which tools are best suited for different scenarios.