It has been our pleasure to bring you relevant information on how to simplify predictive analytics through a common standard and deployment process. In this newsletter, we cover our Teradata / Zementis white paper that not only discusses the general benefits of in-database scoring but also highlights the stunning performance that massively-parallel in-database scoring delivers.
Need millions of predictions per second? No problem!
We also wanted to let you know that the recording of our recent webinar with Hortonworks showcasing predictive analytics on Hadoop is now available on-demand.
CEO, Zementis Inc.
Zementis/Teradata White Paper: Massively Parallel In-database Predictions using PMML
Open standards enable interoperability and portability across systems and solutions. Such a level of flexibility creates new opportunities for addressing exceedingly demanding business agility and performance requirements. The Predictive Model Markup Language (PMML) is the embodiment of an open standard and delivers such benefits in the world of data mining and predictive analytics. This means that models developed in any environment and tool set can be deployed and used in a completely different system.
In the context of Big Data, the urgent need to apply the power of predictive analytics to derive reliable predictions-and, hence, business decisions-from vast amounts of data collected by many organizations is a key requirement. In this paper, we discuss how the PMML standard enables embedding advanced predictive models directly into the database or the data warehouse, alongside the actual data to be scored. More importantly, we show how we can easily take advantage of a highly parallel database architecture to efficiently derive predictions from very large volumes of data.
Zementis/Hortonworks Webinar Available On-demand
Join Ofer Mendelevitch, Director of Data Science of Hortonworks, and Michael Zeller, Founder and CEO of Zementis, as they present key learnings as to what drives successful implementations of Big Data analytics projects.
In this webinar, Hortonworks presents their approach to using Apache Hadoop for predictive models with Big Data, and the benefits of Hadoop to data scientists. Zementis demonstrates how to quickly deploy, execute, and optimize predictive models from open source machine learning tools like R and Python as well as commercial data mining vendors like IBM, SAP and SAS.
Whether your company is just beginning to work with predictive analytics or has an experienced data science team this webinar provides valuable insights on how to move predictive models into an operational environment based on Hadoop and Hive and using open industry standards while eliminating the custom coding and delays typically associated with these projects. If you missed the live event, please make sure to view it on-demand.