Batch Machine Learning (BML) reaches its limits when dealing with very large
amounts of streaming data. This is especially true for available memory,
handling drift in data streams, and processing new, unknown data. Online
Machine Learning (OML) is an alternative to BML that overcomes the limitations
of BML. OML is able to process data in a sequential manner, which is especially
useful for data streams. The `river` package is a Python OML-library, which
provides a variety of online learning algorithms for classification,
regression, clustering, anomaly detection, and more. The `spotRiver` package
provides a framework for hyperparameter tuning of OML models. The
`spotRiverGUI` is a graphical user interface for the `spotRiver` package. The
`spotRiverGUI` releases the user from the burden of manually searching for the
optimal hyperparameter setting. After the data is provided, users can compare
different OML algorithms from the powerful `river` package in a convenient way
and tune the selected algorithms very efficiently.

批量机器学习在处理大量流数据时存在困难，但在线机器学习是一种解决批量机器学习限制的替代方法。river 和 spotRiver 是 Python 中的两个在线学习库，提供了分类、回归、聚类、异常检测等多种在线学习算法，并通过 spotRiverGUI 提供图形用户界面来简化用户的超参数调优过程。