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Tuesday , 14 August 2018
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DevOps and Machine Learning

Machine learning is a branch of computer science that allows a computer to “remember” certain events and use them to statistically predict what might happen next. For example, when a reader consistently selects books from a similar genre or that have similar characteristics, the algorithm’s set up will suggest similar books or authors that might also be of interest to the reader.

Problems with Machine Learning

The problem with machine learning is that it will only go so far. For example, a reader who is passionately fond of Patricia Brigs “Steal the Dragon” might be completely revolted by “Worm Ouroboros” by E.R. Eddison. While both are fantasy and both deal with dragons, the tone and feel of the two fantasy novels are completely different.

The importance of a Good DevOps Consulting Team

It’s for this reason that a good DevOps consulting team will incorporate other means of feedback, such as asking readers to list authors that they believe are similar to their well-loved book. By incorporating added feedback annoying errors such as this can slowly be eliminated. Or, to put it another way, the more data a computer has, the better its predictions will become.

Machine Learning Best Practices

If the machine learning program is set up to make customer recommendations, this is an important aspect of predictive recommendations if the sales team hopes to use automatic recommendations to drive book sales – or sales of any other sort.
With that said, setting up programs so that a computer will retain certain behaviors helps reduce repetitive events, such as the steps needed to boot a computer and load a landing page that will allow the user to quickly go to a required work area. With every repeat action, the computer will become more likely to go to the correct location or to make sensible recommendations.

Machine Learning Summarized

Machine learning is certainly not the whole of DevOps, but it certainly feeds into the process and it follows the principle of automating as many processes as are reasonably possible. Of course, machines will only recommend according to the data they have collected. If they have insufficient or have received erroneous information, they will return nonsensical or less than useful information or even set up obstructive operations. This is where providing methods for feedback becomes important – as well as the means for human intervention.

Catch Those DevOps Errors!

A good DevOps consultation team can catch those errors and nonsensical responses or address them when they are brought to their attention by users. They can sometimes even anticipate the problem and set up checkpoints for computerized operations.

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