- H2O.ai announced the availability of its artificial intelligence (AI) platform on Microsoft Azure HDInsight, making it more widely available for health IT infrastructure deployment. H2O.ai’s Sparkling Water solution is now available on a HDInsight cluster along with other Azure services.
Enterprises can now integrate H2O and Azure HDInsight technologies to help organizations build better open analytics strategies.
Sparkling Water is an open source machine learning solution that works with Spark 2.0, sparklyr, and PySpark. The solution allows users to combine the fast, scalable machine learning algorithms with the capabilities of Spark.
Sparkling Water provides a machine learning platform for developers so users can drive computation from Scala/R/Python and utilize the H2O Flow UI.
H2O.ai’s solution now operates with Azure HDInsight, which is a fully managed cloud Hadoop offering that optimizes open source analytical clusters for Spark Hive, MapReduce, HBase, Storm, Kafka, and R Server.
Hadoop is an open-source distributed data storage and analytics application available on Azure HDInsight. Hadoop is not a data warehouse, but instead acts as a software framework to handle structured and unstructured data.
Hadoop distributes large amounts of data to different processing nodes, then combines the collected results. This approach allows data to be processed faster because the system is working with smaller batches of localized data instead of the contents of the entire warehouse.
The availability of H2O.ai Sparkling Water in Azure HDInsight gives users access to Hadoop capabilities and an advanced analytics platform in the cloud. Users can either install H2O Artificial Intelligence on an existing HDInsight Spark cluster, or install during the creation of a new HDInsight cluster on Azure.
The solution will install Sparkling Water on a Spark cluster so users can leverage benefits from both Spark and H2O. The solution can also access data from Azure Blob storage and/or Azure Data Lake Store in addition to all the standard data sources that H2O supports.
It also provides Jupyter Notebooks with pre-baked H2O examples for an easy jumpstart, and a friendly H2O FLOW UI to monitor and debug the data science applications.
“We want to ensure our customers have a comprehensive machine learning environment, enabling them to get the most value out of their data,” Microsoft Director of Data Platform Product Marketing Joanne Marone said in a statement. “Working with H2O.ai enables Microsoft to offer their AI platform in combination with our own investments in open source analytics on Microsoft Azure HDInsight, providing data scientists and big data engineers with more choice for machine and deep learning on Azure.”
Healthcare organizations are looking to open source solutions as interoperability becomes a more prominent issue. When it comes to analytics, organizations seeking to embrace value-based care initiatives are in need of flexible solutions that will easily communicate and exchange data with other systems. Open source solutions give organizations the opportunity to develop their own strategies for interoperability and usability.
Late last year, Health Catalyst announced the launch of its open source analytics solution to encourage healthcare organizations currently implementing successful AI solutions for predictive analytics to make their solutions widely available.
Health Catalyst’s healthcare.ai was built to make machine learning accessible to healthcare organizations with limited means to develop their own AI analytics solutions. Healthcare.ai will help organizations improve the quality of their patient’s care with predictive analytics without deployment being too costly.
Open-source software benefits healthcare organizations, especially institutions that are unable to afford a full-scale AI deployment. Open-source software does not charge licensing fees for the code, freeing up funds for other IT initiatives and allowing organizations to take advantage of advanced technology to the benefit of patients.