By H2O.ai Team | minute read | March 25, 2014
Big Data has moved in-memory. Customers using SQL in their Join & Munging efforts via SHARK and Apache Spark need to use Regressions and Deep Learning. To make their experiences great & seamlessly weave SQL workflows with Data Science and Machine Learning, we are architecting a simple RDD data import-export in H2O. This brings continuity to their in-memory interactive experience. And support for Spark MLI using our native Scala API – Shalala.
Big Data users can now use SHARK to extract and fuse datasets and H2O for better predictions.
hdfs | Spark | SHARK/SQL | RDD | h2o.readRDD() | h2o.deepLearning() | h2o.predict() | h2o.persist(RDD or HDFS)
Calling h2o.deepLearning() from within Scala interface alongside Spark (via Shalala) will make the workflow even more seamless for end users.
At H2O.ai, democratizing AI isn’t just an idea. It’s a movement. And that means that it requires action. We started out as a group of like minded individuals in the open source community, collectively driven by the idea that there should be freedom around the creation and use of AI. Today we have evolved into a global company built by people from a variety of different backgrounds and skill sets, all driven to be part of something greater than ourselves. Our partnerships now extend beyond the open-source community to include business customers, academia, and non-profit organizations.