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| H2O | |
|---|---|
 | |
| Original authors | SriSatish Ambati, Cliff Click |
| Developer | H2O.ai |
| Initial release | 2011 |
| Stable release | 3.46.0.2
/ 13 May 2024 |
| Repository | |
| Written in | Java, Python, R |
| Operating system | Unix, Mac OS, Microsoft Windows |
| Type | Statistics software |
| License | Apache License 2.0 |
| Website | www.h2o.ai |
H2O is an open-source, in-memory, distributed machine learning and predictive analytics platform developed by the company H2O.ai (previously 0xdata). The software is designed to operate on commodity hardware clusters and supports algorithms for large-scale data analysis and model deployment.
H2O is primarily used by data scientists and developers for statistical modeling and data-driven decision-making. The platform is designed to handle in-memory computations across a distributed computing environment. It offers implementations for numerous statistical and machine learning algorithms, which are accessible through various programming interfaces.
The software is released under the Apache License 2.0.
Functionality and features
[edit]H2O provides a suite of supervised and unsupervised machine learning algorithms. Its core functions include:
- Supervised learning: algorithms in the field of statistics, data mining and machine learning such as generalized linear models, random forests, gradient boosting and deep learning are implemented for classification and regression tasks.[1]
- Unsupervised learning: including K-Means clustering and principal component analysis.
- Automated machine learning: a features designed to automate the processes of model selection, tuning, and ensemble creation.
The software can ingest data from various sources, including the Hadoop Distributed File System, Amazon S3, SQL databases, as well as local file systems. It operates natively on Apache Spark clusters through Sparkling Water. Proponents claim that improved performance is achieved compared to other analysis tools.[2] The software is distributed free of charge, under a business model based on the development of individual applications and support.[3]
Architecture
[edit]H2O is primarily written in Java. It uses a distributed architecture that allows the platform to cluster nodes for parallel processing and in-memory storage of data and models.
Users interact with the H2O platform through several primary interfaces:
- Programming language interfaces: APIs are provided for the R and Python programming languages, and various Apache offerings (Apache Hadoop and Spark, as well as Maven).
- H2O Flow: a graphical web-based interactive computational environment that functions as a notebook interface for data exploration, model building, and scripting.
- REST-API: allows for integration with other applications and frameworks such as Microsoft Excel or RStudio.[4] With the H2O Machine Learning Integration Nodes, KNIME offers algorithmic workflows.[5]
While the algorithm executes, approximate results are displayed, so that users can track the progress and intervene if needed.
History, influences, and extensions
[edit]The software project was initiated by the company 0xdata, which later changed its name to H2O.ai. The three Stanford professors Stephen P. Boyd, Robert Tibshirani and Trevor Hastie form a panel that advises H2O on scientific issues.[6] Since its inception, H2O has focused on providing open-source tools to facilitate the deployment of machine learning environments in enterprise environments. The core H2O platform is often complemented by offerings from H2O.ai, such as H2O Driverless AI.
Accolades
[edit]H2O was voted number one by GitHub members among the open source machine learning projects written in Java. Fortune magazine also named Arno Candel (one of the most important developers) as one of 20 Big Data All-Stars in 2014.[7]
External links
[edit]References
[edit]- ^ Dulhare, U. N., Ahmad, K., Ahmad, K. A. B., Dulhare, U. N., Mubeen, A., Ahmad, K. (15 July 2020). Hands-On H2O Machine Learning Tool. Wiley. pp. 423–453. doi:10.1002/9781119654834.ch15. ISBN 978-1-119-65474-2.
- ^ Cook, Darren (2016-12-05). Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI. "O'Reilly Media, Inc.". ISBN 978-1-4919-6457-6.
- ^ Jordan Novet (2014-11-07). "0xdata takes $8.9M and becomes H2O to match its open-source machine-learning project". VentureBeat. VentureBeat, Inc. Archived from the original on 2022-10-24. Retrieved 2016-07-23.
- ^ Ajgaonkar, Salil (2022-09-26). Practical Automated Machine Learning Using H2O.ai: Discover the power of automated machine learning, from experimentation through to deployment to production. Packt Publishing Ltd. ISBN 978-1-80107-635-7.
- ^ "KNIME Hub". Retrieved 2020-01-23.
- ^ "Start Off 2017 with Our Stanford Advisors | H2O.ai". h2o.ai. Retrieved 2024-11-09.
- ^ Andrew Nusca, Robert Hackett, Shalene Gupta; Arno Candel, Physicist and Hacker, 0xdata (2014-08-03). "Meet Fortune's 2014 Big Data All-Stars". Fortune. Time Inc. Retrieved 2016-07-23.
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