Running H2O on Azure HDInsight

Apr 20, 2017 · Dallas, United States of America

Please register here to attend the webinar. 

H2O.ai is excited to announce that its AI platform is now available on Microsoft Azure HDInsight. Customers can now use H2O.ai’s Sparkling Water solution on a HDInsight cluster along with Azure’s collection of cloud services. 

In this webinar, we’ll walk through the process of setting up an HDI cluster with Spark and Sparkling Water, ingest data from Azure blob storage, clean and prep the data using Spark and build a model with Sparkling Water. 

We’ll also demonstrate the ease of use exporting the models and pipeline to scoring service or engine for inference. We’ll use Jupyter notebooks and H2O Flow for the entire workflow 

Speaker Bio: 

Xiaoyong Zhu is a Program Manager working on the Microsoft HDInsight team. He is currently focusing on machine learning on top of big data. You can reach him at [masked]. 

Vinod Iyengar is Director of Business Development and Partnerships. Vinod comes with over 7 years of Marketing & Data Science experience in multiple startups. He was the founding employee for his previous startup, Activehours, where he helped build the product and bootstrap the user acquisition with growth hacking. He has seen the user base for his companies grow from scratch to millions of customers. He’s built models to score leads, reduce churn, increase conversion, prevent fraud and many more use cases. 

He brings a strong analytical side and a metrics-driven approach to marketing. He is responsible for all of our demand generation and growth efforts. When he is not busy hacking, Vinod loves painting and reading. He is a huge foodie and will eat anything that doesn’t crawl, swim or move. 

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  • Dallas Big Data Science!

    This meetup is about sharing knowledge & learning the latest developments about statistics, machine learning, math, analytics, parallel algorithms, distributed systems. We will do hands on work with Data and invite experts in the space to discuss practical and theoretical aspects of doing data science with big data.

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