Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 Min Read
    big data analytics in transporation
    Turning Data Into Decisions: How Analytics Improves Transportation Strategy
    3 Min Read
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Operational Deployment of Predictive Solutions: Lost in Translation? Not with PMML
Share
Notification
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
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.

More Read

Business Rules Forum
They Know What You’re Watching (And Why You’re Watching it)
IBM Emphasizes The Benefits Of Data Analytics For Renewable Energy
Analytics Revolution: Why the Struggle for Growth?
Digital data explosion highlights need for new-age Database and Business Intelligence technologies


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!

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Why the AI Race Is Being Decided at the Dataset Level
Why the AI Race Is Being Decided at the Dataset Level
Artificial Intelligence Big Data Exclusive
image fx (60)
Data Analytics Driving the Modern E-commerce Warehouse
Analytics Big Data Exclusive
ai for building crypto banks
Building Your Own Crypto Bank with AI
Blockchain Exclusive
julia taubitz vn5s g5spky unsplash
Benefits of AI in Nursing Education Amid Medicaid Cuts
Artificial Intelligence Exclusive News

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Update on Predictive Analytics in the Cloud

2 Min Read

Granola: Disruptive Technology without the Disruption

0 Min Read
Telecom big data hadoop
AnalyticsBig DataBusiness IntelligenceCloud ComputingData MiningData QualityData VisualizationData WarehousingHadoopHardwareITMapReduceOpen SourceSoftwareSQLUnstructured DataWorkforce Data

Data Lakes and Network Optimization: What’s Next for Telecommunications and Big Data

6 Min Read

5 ways to reduce cost with predictive analytics

6 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 is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?