Wednesday, May 28, 2014

Creating, Modifying, Deploying and Scoring Predictive Models with PMML, ADAPA and KNIME

Zementis and KNIME co-presented a webinar on creating, modifying, deploying and scoring predictive models using PMML. The webinar is now available for viewing on-demand (see below). It starts with Iris Adae from KNIME giving an overview of PMML, the Predictive Model Markup Language standard as well as the extensive support KNIME offers for PMML. PMML is the de facto standard for predictive analytics and can be produced by KNIME for a number of modeling techniques as well as data pre-processing nodes/computations.

Iris' presentation and demo are then followed by Alex Guazzelli from Zementis who shows how easy it is for anyone to benefit from models built in KNIME or R and deployed in the Zementis ADAPA Scoring Engine for execution. Once uploaded in ADAPA, models are available for scoring via web-services (SOAP or REST). KNIME can then be used to connect to a database, read in data and pass it through ADAPA for scoring via the REST API.

 On-demand webinar (available on YouTube):

 

The idea presented in this webinar is to show how easy one can easily move a predictive model from the scientist's desktop to the IT operational environment. When training a model, scientists rely on historical data, but when using the model on a regular basis, the model is moved or deployed in production where it presented with new data. ADAPA provides a scalable and lightning fast scoring engine for models that live in production. And, although KNIME data mining nodes are typically used by scientists to build models, its database and REST nodes as well as PMML-enabled nodes can simply be used to create a flow for passing models and data for scoring in ADAPA. 

Use-cases discussed are:

  • Read data from a flat file, use KNIME for data pre-processing and building of a neural network model. Export the entire predictive workflow as a PMML file and then take this PMML file and upload and score it in ADAPA via its Admin Web Console. 
  • Read data from a database (MySQL, SQLServer, Oracle, ...), build model in KNIME, export model as a PMML file and deploy it in ADAPA using its REST API. This use-case also shows new or testing data flowing from the database and into ADAPA for scoring via a sequence of KNIME nodes. 
  • The video also shows a case in which one can use KNIME nodes to simply read a PMML file produced in any PMML-compliant data mining tool (R, SAS EM, SPSS, ...), upload it in ADAPA using the REST API and score new data from MySQL in ADAPA also through the REST interface. Note that in this case, the model has already been trained and we are just using KNIME to deploy the existing PMML file in ADAPA for scoring. 

 To watch the Zementis discussed use-cases only, watch:

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