Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies by Steven Finlay

Author:Steven Finlay
Language: eng
Format: epub, mobi, pdf
Publisher: Relativistic
Published: 2018-06-29T18:30:00+00:00

Implementation. What system or process will be used to put the model into practice? How will scores be calculated? How will decisions made on the basis of those scores be acted upon by the relevant business function?

Development Data. Does sufficient data exist to enable predictive models to be constructed?

Analytical capability. Does the organization have the software tools and expertise required to analyze data, apply machine learning and build good quality, usable predictive models?

At first sight, the ordering of this list may seem somewhat counterintuitive. Why, for example, is model implementation listed before the analytics? Surely one builds a model first and then thinks about implementing it?

Machine learning is good for a lot of things, but it’s not always the case that an automated machine learning based solution is what you need. Likewise, just because it’s technically possible to predict something it doesn’t mean that you should or that the predictions will prove valuable. Therefore, one needs to begin with problem specification and a plan about how to address it. Thought needs to be given to how you are going to use machine learning within an organization before you apply it and so on.

If machine learning is going to be the answer to the problem, then the two critical items which need to be established before anything else is done are:

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