Two AI Paths Pondered by Teradata

December 20, 2015

I read the content marketing write up by Karthik Guruswamy. I like the “guru” part of the expert’s name. I am stuck with the “old” part of my name.

The write is called “Data Science: Machine Learning Vs. Rules Based Systems.” I know a little bit about both of these methods, and I know a teeny tiny bit about Teradata, an outstanding data warehouse solution chugging along with its stock in the high $20s per share. The Google finance chart suggests that the company has some challenges with net income and profit margin to my unlearned eye:

image

Looks like some content marketing oomph is needed to move that top line number.

I learned in the write up:

Rules based systems will work effectively if all the situations, under which decisions can be made, are known ahead of time.

Okay. Insight. Know everything ahead of time and one can write rules to cover the situation. Is this expensive? Is this a never ending job? Consultants sure hope so.

There is an alternative:

Enter Machine Learning or ML! If we classify the data into good vs. bad data sets or categorize them into different labels like A, B, C, D etc., the Machine Learning algorithms can help build rules on the fly. This step is called training which results in a model. During operationalization, this model is used by the prediction algorithm to classify the incoming data in the right way which in turn leads to sound decision making.

I recall that Autonomy used this approach for its system. Those familiar with Autonomy have some experience with retraining, Bayesian drift, and other exciting facets of machine learning based systems. Consultants love to build new training sets.

The write up asserts:

With Machine Learning, one can iteratively achieve good results by cleansing & prepping the data, changing or combining algorithms or merely tweaking the algorithm parameters. This is becoming much easier thanks to the increased awareness and the availability of different types of data science tools in the market today.

High five.

My view is that the write up left out some information. But there is one omission which warrants a special comment.

Neither of these systems works without human intervention.

Bummer. Reality is sort of a drag, but maybe that’s why Teradata is wrestling with revenue and net profit alligators. Consultants, on the other hand, can bill to enhance either approach.

What about the customer? Well, some customers of brand name data warehouse systems struggle to get data into and out of this whiz bang systems in my experience. Regardless of the craziness involved with Hadoop and Spark, these open source approaches may make more sense than pumping six or seven figures into a proprietary system.

Consultants can still bill, of course. That’s one upside of any approach one wishes to embrace.

Stephen E Arnold, December 20, 2015

Comments

One Response to “Two AI Paths Pondered by Teradata”

  1. Dinesh Vadhia on December 20th, 2015 6:25 pm

    Haven’t read the article but this 2014 paper from Google “Machine Learning: The High Interest Credit Card of Technical Debt (http://research.google.com/pubs/pub43146.html) has been getting increasing play because it points out clearly that ML today carries many costs when deployed in production systems.

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