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.

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