Machine learning (ML) can provide unique analytical insights, as well as help automate some operational and decision-making processes more efficiently and effectively than non-ML alternatives. However, ML is also among the buzziest of buzzwords, and many are overselling and oversimplifying its usage.

Do not let anyone frame a data analysis, business problem, or process improvement as an ML use case. Instead, say: That is Not Machine Learning — that is a data analysis, business problem, or process improvement where ML might be able to help. But not before we evaluate other options. And with the understanding that ML is rarely going to be either the first or only aspect of the solution.

This episode is sponsored by: Vertica.com

Extended Show Notes: ocdqblog.com/dbp

Follow Jim Harris on Twitter: @ocdqblog

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Label Making. That is my simple two-word definition of Machine Learning. Machine Learning is Label Making. ML is LM.

Especially supervised machine learning, which creates either numerical labels (using regression algorithms) to make predictions about a continuous data value (such as sale or stock prices), or categorical labels (using classification algorithms) to assign data to pre-defined groups also called classes (such as Fraud or Not Fraud for financial transactions).

This episode is sponsored by: Vertica.com

Extended Show Notes: ocdqblog.com/dbp

Follow Jim Harris on Twitter: @ocdqblog

Email Jim Harris: ocdqblog.com/contact

Other ways to listen: bit.ly/listen-dbp

Based on one of my presentations, this episode provides a five-part vendor-neutral framework for evaluating the critical capabilities of a cloud data analytics solution: Deploy, Store, Optimize, Analyze, Govern.

This episode is sponsored by: Vertica.com

Extended Show Notes: ocdqblog.com/dbp

Follow Jim Harris on Twitter: @ocdqblog

Email Jim Harris: ocdqblog.com/contact

Other ways to listen: bit.ly/listen-dbp