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is an open source machine learning platform for the enterprise. The distributed in-memory machine learning platform with linear scalability supports the most widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry-leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

H2O works on existing big data infrastructure, on bare metal or on top of existing Hadoop or Spark clusters. It can ingest data directly from HDFS, Spark, S3, Azure Data Lake or any other data source into its in-memory distributed key value store. H2O takes advantage of the computing power of distributed systems and in-memory computing to accelerate machine learning using itís industry parallelized algorithms which take advantage of fine grained in-memory map reduce.

To learn more about the capabilities of H2O, read our case study to see how Change Healthcare uses H2O for claims.

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