By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    customer experience analytics
    Using Data Analysis to Improve and Verify the Customer Experience and Bad Reviews
    6 Min Read
    data analytics and CRO
    Data Analytics is Crucial for Website CRO
    9 Min Read
    analytics in digital marketing
    The Importance of Analytics in Digital Marketing
    8 Min Read
    benefits of investing in employee data
    6 Ways to Use Data to Improve Employee Productivity
    8 Min Read
    Jira and zendesk usage
    Jira Service Management vs Zendesk: What Are the Differences?
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Operational Deployment of Predictive Solutions: Lost in Translation? Not with PMML
Share
Notification Show More
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > Operational Deployment of Predictive Solutions: Lost in Translation? Not with PMML
AnalyticsPredictive Analytics

Operational Deployment of Predictive Solutions: Lost in Translation? Not with PMML

MichaelZeller
Last updated: 2011/12/16 at 8:16 PM
MichaelZeller
3 Min Read
SHARE

Traditionally, the deployment of predictive solutions have been, to put it mildly, cumbersome. As shown in the Figure below, data mining scientists work hard to analyze historical data and to build the best predictive solutions out it. Engineers, on the other hand, are usually responsible for bringing these solutions to life, by recoding them into a format suitable for production deployment.

Traditionally, the deployment of predictive solutions have been, to put it mildly, cumbersome. As shown in the Figure below, data mining scientists work hard to analyze historical data and to build the best predictive solutions out it. Engineers, on the other hand, are usually responsible for bringing these solutions to life, by recoding them into a format suitable for production deployment. Given that data mining scientists and engineers tend to inhabit different information worlds, the process of moving a predictive solution from the scientist’s desktop to production can get lost in translation.


Luckily, the advent of PMML (Predictive Model Markup Language) changed this scenario radically. PMML is the de facto standard used to represent predictive solutions. In this way, there is no need for scientists to write a word document describing the solution. They can just export it as a PMML file. Today, all major data mining tools and statistical packages support PMML. These include IBM SPSS, SAS, R, KNIME, RapidMiner, KXEN, … Also, tools such as the Zementis Transformations Generator and KNIME allow for easy PMML coding for pre- and post-processing steps.

Great! Once a PMML file exists, it can be easily deployed in production with ADAPA, the Zementis scoring engine. ADAPA even allows for models to be deployed in the Amazon Cloud and be accessed from anywhere via web-services. Zementis also offers in-database scoring via its Universal PMML Plug-in, which is also available for Hadoop. In this way, a process that could take 6 months, now takes minutes.


PMML and ADAPA have transformed model deployment forever. If you or your company are still spending time and resources in deploying your predictive analytics the traditional way, make sure to contact us. The secret behind exceptional predictive analytics is out!

MichaelZeller December 16, 2011 December 16, 2011
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Cloud-Based Marketing
Smart Video Bloggers Are Leveraging Cloud-Based Marketing Tools
Cloud Computing IT Marketing
technology and security
Technology in Physical Security: A Guide to Business Safety
Exclusive IT Security
ai for stopping credit card theft
AI Can Manage Credit Card Cybersecurity Risks
IT Security
ai can help with nurse burnout
Breakthroughs in AI Are Helping to Prevent Nurse Burnout
Artificial Intelligence Exclusive

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

customer experience analytics
Analytics

Using Data Analysis to Improve and Verify the Customer Experience and Bad Reviews

6 Min Read
data analytics and CRO
Analytics

Data Analytics is Crucial for Website CRO

9 Min Read
analytics in digital marketing
Analytics

The Importance of Analytics in Digital Marketing

8 Min Read
benefits of investing in employee data
Analytics

6 Ways to Use Data to Improve Employee Productivity

8 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?