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.

Looking to meet enterprise needs in the machine learning space, Oracle is making its Tribuo Java machine learning library available free under an open source license. With Tribuo, Oracle aims to make it easier to build and deploy machine learning models in Java, similar to what already has happened with Python. Released under an Apache 2.0 license and developed by Oracle Labs, Tribuo is accessible from GitHub and Maven Central. Tribuo provides standard machine learning functionality including algorithms for classification, clustering, anomaly detection, and regression. Tribuo also includes pipelines for loading and transforming data and provides a suite of evaluations for supported prediction tasks. Because Tribuo collects statistics on inputs, Tribuo can describe the range of each input, for example.

Tribuo is a Java ML library for multi-class classification, regression, clustering, anomaly detection and multi-label classification.