Using a standards-based approach to deploy predictive analytics on operational systems from mainframes to Hadoop.
Predictive analytics is a powerful tool for managing risk, reducing fraud and maximizing customer value. Organizations succeeding with predictive analytics are looking for ways to scale and speed up their programs and make predictive analytics pervasive. The big challenge for analytics-driven organizations today is closing the gap between deriving an analytic result and getting the ROI. Organizations need a consistent and efficient way to deploy analytic results into everything from systems of record like mainframes to modern big data infrastructure.
Zementis ADAPA is a standards-based predictive analytics scoring engine. It is predominantly used as an analytics platform for rapid operational deployment and execution of predictive models. In addition, the Zementis ADAPA scoring platform can add significant value as a general purpose analytics server for development and testing of machine learning models.
In the context of the R data mining tool, this white paper will discuss how to leverage ADAPA as a development and testing framework in the data science team. ADAPA as an “R Server” is not limited by the size of the data and will take full advantage of available compute resources to provide high-performance analytics. It supports high-throughput scoring for model verification and model validation, especially when comparing complex model types or large collections of models.
As an open-source language, R delivers users both the benefits and disadvantages that open-source languages typically present. The Predictive Model Markup Language (PMML) industry standard can address many of these challenges effectively, and Zementis offers commercial software solutions that are based on the PMML industry standard to deliver enterprise-grade performance, stability and technical support.
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) delivers such benefits in the world of data mining.
An open standards approach that accelerates model deployment to empower big data insights. Zementis and IBM are working together to help organizations find simple, cost-effective ways to accelerate and ease the deployment, execution and integration of predictive analytics into business operations.
Standards play a central role in creating an ecosystem that supports current and future needs for broad, real-time use of predictive analytics in an era of Big Data. In this white paper, James Taylor (CEO, Decision Management Solutions) writes about the role of R, Hadoop and PMML in the mainstreaming of predictive analytics.
Sybase and Zementis have joined together to provide this white paper, which describes how Sybase is removing the barriers that slow the implementation of game-changing big data analytics applications by adopting PMML, the Predictive Model Markup Language.
In a very real sense, standard languages such as PMML, the Predictive Model Markup Language, are unlocking the latent value of big data, and ushering in a new era of real-time analytics. With PMML and its associated technologies, the observation that data is the “new oil” of the 21st century seems entirely plausible. Find out how R users are leveraging the power of PMML to make them instantly available for execution in real-time via the Zementis ADAPA Scoring Engine.
There is a lot of theory and hype around the topics of social media, recommendation engines and real time modeling, but until now not many practical examples that can be measured in terms of ROI. KNIME AG and Zementis have joined together to provide this white paper, which summarizes a practical case study that combines all three topics, and delivers a measured and solid business case.
As advanced analytics becomes pervasive across the enterprise to drive better business decisions, the need for efficient execution of predictive models is paramount. Zementis and Greenplum joined forces to help companies easily bring predictive models into their database and score in-place and in-parallel huge amounts of data. In this whitepaper, we demonstrate how to deploy and execute predictive models using the Zementis Universal PMML Plug-in for Pivotal/Greenplum and give specific examples using models built on IBM SPSS and the open source R program.