Tuesday, August 2, 2016

The Data Mining Group Releases PMML v4.3

Another key milestone for PMML and interoperability in data science!

The Data Mining Group (DMG), a vendor-led consortium of companies and organizations developing standards for statistical and data mining models, announced the general availability of version 4.3 of the Predictive Model Markup Language (PMML):

Chicago, IL 8/2/2016 – The Data Mining Group is proud to announce the release of PMML v4.3. PMML is an application and system independent XML interchange format for statistical and data mining models. The goal of the PMML standard is to encapsulate a model independent of applications or systems using an XML configuration file so that two different applications (the PMML Producer and Consumer) can use it.

“The PMML standard delivers true interoperability, enabling machine learning and predictive models to be deployed across IT platforms,” says Michael Zeller, CEO of Zementis, Inc. “A common standard ensures efficiency and fosters collaboration across organizational boundaries which is essential for data science to scale beyond its current use cases. With its latest release, PMML has matured to the point where it not only has extensive vendor support but also has become the backbone of many big data and streaming analytics applications.”

Read the full press release here

Friday, July 22, 2016

Effective Deployment of AI, Machine Learning and Predictive Models from R

Operational deployment in your business process is where AI, machine learning and predictive algorithms actually start generating measurable results and ROI for your organization. Therefore, the faster you are able deploy and use these “intelligent” models in your IT environment, the more your business will reap in the benefits of smarter decisions.

The Challenge

In the past, the operational deployment of AI, machine learning and predictive algorithms used to be a tedious, labor- and time-intensive task. Predictive and machine learning models, once built by the data science team, needed to be manually re-coded for enterprise deployment in operational IT systems. Only then predictive models could be used to effectively score new data in real-time streaming or big data batch applications.
As you can imagine, this process was prone to errors, could easily take up to six months or more, and it wasted valuable resources. Not only did it limit how fast models could be deployed, but also made it difficult to leverage more complex machine learning algorithms that could deliver more precise results.
Given such challenges, how can we achieve a more efficient model development life cycle, for example with R, which is one of the most popular open source data mining tools?

A Standards-based Solution

The answer is PMML, the Predictive Model Markup Language industry standard. PMML is an XML-based standard for the vendor-independent exchange of predictive analytics, data mining and machine learning models. Developed by the Data Mining Group, PMML has matured to the point where it now has extensive vendor support and has become the backbone of big data and streaming analytics. For today’s agile IT infrastructure, PMML delivers the necessary representational power for predictive models to be quickly and easily exchanged between systems.
One of the leading statistical modeling platform today is R. R allows for quick exploration of data, extraction of important features and has available a large variety of packages which give data scientists easy access to various modeling techniques. The ‘pmml’ package for R was created to allow data scientists to export their models, once constructed, to PMML format. The latest version of this package, v1.5, contains various new functions providing the modeler a more interactive access to the PMML constructed; they can now modify the PMML after it was constructed to a greater degree.
For the R experts among the readers, the following series of posts describes in more detail some of the new functions implemented and their uses:
  1. R PMML helper functions to modify the MiningField element attributes
  2. R PMML helper functions to modify the DataDictionary element attributes
  3. DataDictionary Helper Functions II
  4. PMML Post-processing: Output Helper Function
For a more basic introduction to R, we invite you to download a free infographic and white paper.


The next step, of course, would be to upload your own PMML models into an operational platform. If you are ready for that and want to see how easy it is to deploy and score your models, please check out the free trial of the ADAPA Decision Engine on the AWS Marketplace.

Monday, July 27, 2015

Zementis Announces Predictive Analytics Integrated with IBM z Systems for Insight-driven Business Processes

Quote from Ross Mauri 
Zementis, Inc. and IBM Corporation (NYSE: IBM) today announced a joint strategic initiative and corresponding technology solution, “Zementis for IBM z Systems”, designed for companies seeking to optimize business decisions and formulate those decisions faster. The solution seeks to unlock the full potential of an organization’s data assets by integrating predictive analytics capabilities directly into transactional data flows that drive business processes. 
The joint offering combines Zementis’ solution for high-speed development, deployment and operation of predictive analytics models with IBM z Systems, a family of next-generation mainframe computing platforms that help organizations reinvent enterprise IT to become digital businesses. Zementis for IBM z Systems enhances IBM’s capabilities by integrating predictive analytics directly into core business processes, delivering timely predictive insights directly to the point of maximum business impact.

Monday, July 20, 2015

Zementis: Big Data Insights through “True” Analytics

Scoring for Everyone and Everything 


In the past, the three constituents in the “making sense of data for the future” pool (data scientists, IT professionals and business users) have suffered from a lack of collaboration, inefficiencies in communication, and ineffectiveness at uncovering valuable insights for their enterprises.

Zementis presents a solution to help enterprises to bridge the gap between these three groups, or even better, to align them to create better insights for their enterprises. At the center of this effort is the portability of analytical models. Zementis has been a fervent supporter of PMML (Predictive Mode Mark-up Language) on its products. That is the leading analytical model portability standard.

This vendor profile provides an overview of Zementis and identifies key differentiators, product offerings, and a short list guide for buyers. 

Tuesday, June 23, 2015

Software AG and Zementis Announce Partnership and Integrated Solution for Predictive Analytics

Software AG, a leading global provider of software and IT services, today announced a business and technology partnership with Zementis, as part of Software AG’s strategy and market offerings for big data predictive analytics.
Software AG and Zementis share a common philosophy and vision for the potential of advanced predictive analytics, and have therefore decided to forge a formal partnership based on this mutual understanding. The relationship is both a technology integration and a collaborative business partnership.
As part of this effort, Software AG has embedded Zementis ADAPA, a standards-based deployment and scoring engine for predictive analytics, within the Software AG Apama Streaming Analytics platform. The two companies will also collaborate on technical solution development, business development and technology enablement for their customers worldwide, spanning multiple industry segments and use cases. The companies will focus initially on use cases related to:
  • Internet-of-Things
  • Banking and Insurance
  • Retail
  • Manufacturing
The announcement is part of Software AG’s effort to enrich and expand its Digital Business Platform – a service-oriented development environment with numerous high-level, cloud-capable application services.
The integrated solution is available now.
To read the Software AG press release, click here.
To read a related post on the Software AG corporate blog, click here.

Wednesday, March 11, 2015

Zementis and Cognizant Partner to Bring Advanced Analytics to Cognizant Clients

On March 10, Cognizant Technology Solutions and Zementis signed a formal partnership agreement, launching a collaborative relationship that will bring advanced analytics solutions that include Zementis technologies to Cognizant’s clients.
cognizant-technology_200x200The collaborative relationship will focus on integrating Zementis’ predictive analytics technologies with the advanced analytics capabilities of Cognizant Analytics, developing business solutions based on advanced analytics for Cognizant clients, and jointly enabling those solutions to deliver client success. The partnership is both a technology alliance and a channel (go-to-market) arrangement, and has global scope. Zementis and Cognizant Analytics will collaborate with each other and with Cognizant’s market-facing business segments to jointly serve Cognizant clients. Initial efforts will focus on two industry verticals: Banking & Financial Services and Life Sciences & Healthcare. Over time, the industry focus will expand to encompass Cognizant’s other industry solution groups: Insurance, Communications, Media & Technology and Products & Resources.
The partnership will encompass Zementis’ entire product portfolio, including its two core solutions, ADAPA® and UPPI, as well as multiple platform-specific variants. Cognizant clients will be able to deploy these solutions on-premise or in the cloud, with access via an intuitive Web-based console, via one of multiple industry-leading analytics platforms or as a simplified Hadoop interface.
For more information, read the press release.

Thursday, February 19, 2015

ADAPA for Azure: Enthusiasm in the Market, Excitement at Microsoft

In October of last year, Zementis was proud to announce that our flagship product ADAPA® had been certified for Microsoft’s Azure cloud platform. Well, we’re still proud of that accomplishment, and we’re even prouder to see strong interest in the market and enthusiastic support from Microsoft!

Since launching ADAPA on Azure, Zementis has observed a particularly high level of interest from existing and prospective customers, as well as from other key market players active in big data analytics. Companies, government entities and other types of organizations that rely on predictive analytics to give them accurate insights into future outcomes are clearly understanding the value of the Azure cloud, and also of using Zementis ADAPA to power their predictive analytics activities in the cloud.
Microsoft’s Azure leadership team is also weighing in on the value of ADAPA for Azure.
Kim Akers
Kim Akers
General Manager, Microsoft
“Microsoft is excited to welcome Zementis into the Azure community,” said Kim Akers, General Manager at Microsoft. “Zementis ADAPA on Azure enables users to develop and deploy predictive analytics models quickly, and then utilize the resulting predictive data in a variety of ways, both within the Azure ecosystem and beyond.”

Based on initial customer and market response, Zementis believes that ADAPA for Microsoft Azure will generate strong customer demand throughout 2015, laying the groundwork for further significant growth in 2016 and beyond.
“Companies that employ predictive analytics to inform their business decisions can benefit from ADAPA to significantly accelerate data-driven predictive insights, and by using ADAPA on Microsoft Azure, these companies also benefit from the full utility of Microsoft’s cloud platform and partner ecosystem,” says Dr. Michael Zeller, Chief Executive Officer of Zementis. “Together, ADAPA and Azure deliver insight, scalability, stability and security.”
For more information, please visit: www.zementis.com/adapa-for-azure

Tuesday, December 30, 2014

Scoring data with ADAPA using Pentaho Data Integration

Predictive model integration for MySQL, Microsoft SQL Server, Oracle and PostgreSQL


The main use of predictive models is to generate predictions for new data. This data frequently resides in databases like MySQL, and the ADAPA scoring engine needs a way to easily access it. One way of accomplishing this is by using the Pentaho Data Integration (PDI) tool, and in this post we outline how to score data from relational databases using the ADAPA REST API and PDI.

PDI provides an easy to use point-and-click interface to manage the whole workflow: retrieving the data, scoring it through ADAPA, and saving the results elsewhere. It is possible to use PDI to read and write to different databases, including MySQL, Microsoft SQL Server, Oracle, PostgreSQL, and others. PDI can also act as a client to the ADAPA Scoring Engine by leveraging the ADAPA REST API, and take care of transforming the data into necessary formats - JSON and URL in this case.

Prior to starting, we assume that:
  • PDI is installed
  • Data to be scored is stored in either MySQL, Microsoft SQL Server, Oracle or PostgreSQL
  • A PMML model for the data is deployed and available through the ADAPA REST API.

The process is built and executed in PDI. The transformation should consist of the following steps:
  • Retrieve data from the database
  • Transform to a JSON object
  • Convert the JSON object to a URL as a method to transmit it
  • Send URL to ADAPA through REST API
  • Capture ADAPA output
  • Write the scoring result back to a flat file

For detailed step-by-step instructions using a neural network model deployed in ADAPA, please review the following videos:


Monday, December 29, 2014

Using MySQL as a Client to the ADAPA Scoring Engine

Predictive analytics scoring with MySQL and ADAPA


In this blog post, we outline how to use a MySQL database as a client to the ADAPA Scoring Engine by leveraging the ADAPA REST API to execute a predictive analytics model based on the Predictive Model Markup Language (PMML) industry standard.

We assume that:
  • MySQL and cURL are installed
  • Necessary MySQL tables are already created
  • A PMML model for the data is deployed and available through the ADAPA
  • REST API.
One option to make API calls from MySQL is by using the MySQL-UDF-HTTP package, which enables creation of user defined functions for HTTP REST Operations in a database. This package is available on Google Code and will be installed on top of MySQL. We can leverage the User Defined Functions (UDFs) created with this package to make REST API calls to ADAPA from MySQL. Specifically, we use HTTP GET requests to the ADAPA engine to score one record at a time. An advantage of using these functions is that we can easily write the scores back to the database.

In addition, the scoring process can be automated with database triggers. Triggers automatically execute database queries when specified events occur. In this case, we can write functions to score and update or insert records, and set triggers to execute these functions on update and insert events. The HTTP UDF is called by the scoring function to send a GET request to the ADAPA REST API.

Simply using SQL and UDFs, the above enables us to easily execute complex predictive analytics models directly from one of the most commonly used databases, score the records, and write the results back into a database table.

A step-by-step tutorial, including installing MySQL-UDF-HTTP and writing functions and triggers, is available in this video.

Tuesday, December 23, 2014

451 Research Initiates Coverage of Zementis

2014 has been a busy year of growth for Zementis, as our customers, business partners and employees have noticed. Industry analysts have also noticed, with two leading research firms initiating coverage of Zementis in 2014. In May, Gartner included Zementis on its short list of “Cool Vendors in Data Science”, and just this week, 451 Research published its first report on Zementis.

451 Research is well known in the technology sector as a highly respected research and advisory company, and is especially noted for its coverage of emerging technologies and the companies that bring these technologies to market. Within the Enterprise Software sector, Krishna Roy leads 451’s analytical efforts in the realm of business applications and software infrastructure, including big data analytics and predictive analytics. She has a longstanding background in technology journalism, and covers the big data analytics segment extensively. 451’s subscribers will find an amazing number of insightful reports on big data and predictive analytics that bear her byline.

The universe of companies in her analytical portfolio numbers more than 100, affording her a fantastic vantage point from which to study the technologies and market dynamics that shape the competitive landscape and define the market’s evolutionary trajectory. This breadth of coverage also makes her schedule extremely full. Zementis is honored that she has chosen to devote some of her scarce time to studying our company and imparting her perspectives and insights.

To access the report, click on the link below:

Impact Report - December 22, 2014







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