Tribuo：Java中的机器学习
Tribuo: Machine Learning with Provenance in Java
摘要：
机器学习模型部署在广泛的行业中，执行广泛的任务。随着部署的模型数量的增加，跟踪这些模型并确保它们正确运行变得越来越困难。对于影响人类生活的ML系统，也有新的监管负担，需要在高风险情况下将模型与其训练数据联系起来。当前的ML监控系统通常提供来源和实验跟踪作为ML库之上的一层，从而为不完善的跟踪和跟踪对象与元数据之间的偏差留出空间。在本文中，我们将介绍Tribuo，这是一个Java ML库，它将模型训练，推理，强类型安全，运行时检查和自动起源记录集成到一个框架中。Tribuo的所有模型和评估都自动记录输入数据的完整处理流程，以及训练算法、超参数和数据转换步骤。起源存在于模型对象内部，可以使用公共标记格式单独持久化。Tribuo实现了许多流行的ML算法，用于分类，回归，聚类，多标签分类和异常检测，以及XGBoost，TensorFlow和ONNX的接口。Tribuo的源代码可以在https://tribuo.org上获得，并在Apache 2.0许可证下获得文档和教程。https://github.com/oracle/tribuo
英文摘要：
Machine Learning models are deployed across a wide range of industries, performing a wide range of tasks. Tracking these models and ensuring they behave appropriately is becoming increasingly difficult as the number of deployed models increases. There are also new regulatory burdens for ML systems which affect human lives, requiring a link between a model and its training data in high-risk situations. Current ML monitoring systems often provide provenance and experiment tracking as a layer on top of an ML library, allowing room for imperfect tracking and skew between the tracked object and the metadata. In this paper we introduce Tribuo, a Java ML library that integrates model training, inference, strong type-safety, runtime checking, and automatic provenance recording into a single framework. All Tribuo's models and evaluations record the full processing pipeline for input data, along with the training algorithms, hyperparameters and data transformation steps automatically. The provenance lives inside the model object and can be persisted separately using common markup formats. Tribuo implements many popular ML algorithms for classification, regression, clustering, multi-label classification and anomaly detection, along with interfaces to XGBoost, TensorFlow and ONNX Runtime. Tribuo's source code is available at https://github.com/oracle/tribuo under an Apache 2.0 license with documentation and tutorials available at https://tribuo.org.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
