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Why H2O?

H2O makes it possible for anyone to easily apply machine learning and predictive analytics to solve today’s most challenging business problems. It intelligently combines unique features not currently found in other machine learning platforms including:

  • Best of Breed Open Source Technology – Enjoy the freedom that comes with big data science powered by open source technology. 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.
  • Easy-to-use WebUI and Familiar Interfaces – Set up and get started quickly using either 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.
  • Data Agnostic Support for all Common Database and File Types – 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. Install and deploy anywhere, in the cloud, on premise, on workstations, servers or clusters.
  • Massively Scalable Big Data Analysis – Train a model on complete data sets, not just small samples, and iterate and develop models in real-time with H2O’s rapid in-memory distributed parallel processing.
  • Real-time Data Scoring – Rapidly deploy models to production via plain-old Java objects (POJO) or model-optimized Java objects (MOJO). Score new data against models for accurate predictions in any environment. Enjoy faster scoring and better predictions than any other technology.
Combine the power of highly advanced algorithms, the freedom of open source, and the capacity of truly scalable in-memory processing for big data on one or many nodes. These capabilities make it faster, easier, and more cost effective to harness big data to maximum benefit for the business.

Data collection is easy. Decision making is hard. H2O makes it fast and easy to derive insights from your data through faster and better predictive modeling. Existing Big Data stacks are batch oriented. Search and analytics need to be interactive. Use machines to learn machine-generated data. And more data beats better algorithms.

With H2O, you can:

  • Make better predictions. Harness sophisticated, ready-to-use algorithms and the processing power you need to analyze bigger data sets, more models, and more variables.
  • Get started with minimal effort and investment. H2O is an extensible open source platform that offers the most pragmatic way to put big data to work for your business. With H2O, you can work with your existing languages and tools. Further, you can extend the platform seamlessly into your Hadoop environments.

Scalability + Speed

Fine-Grain Distributed Processing on Big Data at Speeds Up to 100x Faster.

Faster H2O lets you model interactively using in-memory processing, and delivers parallel distributed scalability required to support your big data production environments.

The solution combines the responsiveness of in-memory processing with the ability to run fast serialization between nodes and clusters—so you can support the size requirements of your large data sets. Further, H2O does this distributed processing with fine-grain parallelism, which enables optimal efficiency, without introducing degradation in computational accuracy.

In-Memory Processing Responsiveness

With H2O, your organization can harness the responsiveness of highly optimized in-memory processing, so you can operationalize many more models and gain real-time intelligence in business transactions and interactions.

With model export as plain old Java code, you gain lightning fast real-time scoring in any environment.

In addition, the solution enables data scientists to view partial query results while longer processes are running, so they can immediately spot a job that should be stopped and more quickly iterate to find the optimal approach.