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.

翻译：随着AI解决方案在专业环境中的日益普及，开发人员有必要对当前工具格局做出明智决策。本研究通过实证评估多种MLOps（机器学习运维）工具，以促进ML模型生命周期的管理：包括MLflow、Metaflow、Apache Airflow和Kubeflow Pipelines。评估通过两个常见ML场景（基于MNIST的数字分类器和基于IMDB与BERT的情感分类器）的实施，从安装便捷性、配置灵活性、互操作性、代码插装复杂度、结果可解释性及文档质量六个维度对工具进行系统评估。最终通过加权评估结果提供实践性结论，指明不同工具最适用的应用场景。