H2O is an Open Source Fast Scalable Machine Learning API for Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means etc.
Predictive Analytics Software
Best of Breed Open Source Technology
Easy-to-use WebUI and Familiar Interfaces
Data Agnostic Support for all Common Database and File Types
Massively Scalable Big Data Analysis
Real-time Data Scoring
Contact for Pricing
Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
H2O is an Open Source Fast Scalable Machine Learning API for Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means etc.H2O makes it possible for anyone to easily apply machine learning and predictive analytics to solve today’s most challenging business problems. H2O was written from scratch in Java and seamlessly integrates with the most popular open source products like Apache Hadoop and Spark to give customers the flexibility to solve their most challenging data problems. H2O’s intuitive web-based Flow graphical user interface or familiar programming environments like R, Python, Java, Scala, JSON, and through our powerful APIs. Models can be visually inspected during training, which is unique to H2O. Easily explore and model big data from within Microsoft Excel, R Studio, Tableau and more. Connect to data from HDFS, S3, SQL and NoSQL data sources.
Sparkling Water allows users to combine the fast, scalable machine learning algorithms of H2O with the capabilities of Spark. With Sparkling Water, users can drive computation from Scala/R/Python and utilize the H2O Flow UI, providing an ideal machine learning platform for application developers. The Steam AI engine is an end-to-end platform that streamlines the entire process of building and deploying smart applications.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.