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Why Machine Learning Matters

Level 11

While there are many silly depictions of machine learning and artificial intelligence throughout Hollywood, its reality delivers significant benefits. Administrators today oversee so many tasks, like system monitoring, performance optimizing, networking configuration, and more. Many of these tasks can be monotonous and tedious. Also, those tasks are generally required daily. In these cases, machine learning helps ease the burden on the administrator and helps make them more productive with their time. Lately, however, more people seem to think too much machine learning may replace the need for humans to get a job done. While there are instances of machine learning eliminating the need for some tasks to be manned by a human, I don’t believe we’ll see humans replaced by machines (sorry, Terminator fans). Instead, I’ll highlight why I believe machine learning matters now and will continue to matter for generations to come.

Machine Learning Improves Administrator’s Lives

Some tasks administrators are responsible for can be very tedious and take a long time to complete. With machine learning, automation makes the daily tedious tasks run on a schedule and efficiently as system behavior is learned and optimized on the fly. A great example comes in the form of spam mail or calls. Big name telecom companies are now using machine learning to filter out the spam callers flooding cell phones everywhere. Call blocker apps can now screen calls for you based on spam call lists analyzed by machine learning and then block potential spam. In other examples, machine learning can analyze system behavior against a performance baseline and then alert the team of any anomalies and/or the need to make changes. Machine learning is here to help the administrator, not give them anxiety about being replaced.

Machine Learning Makes Technology Better

There are so many amazing software packages available today for backup and recovery, server virtualization, storage optimization, or security hardening. There’s something for every type of workload. When machine learning is applied to these software technologies, it enhances the application and increases the ease of use. Machine learning is doing just that: always learning. If an application workload suddenly increases, machine learning captures it and then will use an algorithm to determine how to react in those situations. When there’s a storage bottleneck, machine learning analyzes the traffic to determine what’s causing the backup and then works out a possible solution to the problem for administrators to implement.

Machine Learning Reduces Complexity

Nobody wants their data center to be more complex. In fact, technology trends in the past 10 to 15 years have leaned towards reducing complexity. Virtualization technology has reduced the need for a large footprint in the data center and reduced the complexity of systems management. Hyperconverged infrastructure (HCI) has gone a step further and consolidated an entire rack’s worth of technology into one box. Machine learning takes it a step further by enabling automation and fast analysis of large data sets to produce actionable tasks. Tasks requiring a ton of administrative overhead are now reduced to an automated and scheduled task monitored by the administrator. Help desk analysts benefit from machine learning’s ability to recognize trending data to better triage certain incident tickets and reduce complexity in troubleshooting those incidents.

Learn Machine Learning

If you don’t have experience with machine learning, dig in and start reading everything you can about it. In some cases, your organization may already be using machine learning. Figure out where it’s being used and start learning how it affects your job day to day. There are so many benefits to using machine learning—find out how it benefits you and start leveraging its power.

Level 13

Thanks for the Article.

Level 13

Thanks for the article!  I think we'll see more growth in machine learning this year.

Level 12

As predictive technology improves we will be able to prevent problems before they happen.

Then we'll have non-tech management see there aren't as many problems, not recognize how much work is going into heading off problems, and cause more problems.


Machine learning or not, any form of automation requires that someone reviews what it is doing from time to time, especially any error logs to make sure it is operating properly.

Level 14

Excellent article.

Learning of any type or form creates knowledge..... Knowledge is power.

The ultimate question is what we do with that knowledge/power? And as Jfrazier​  wisely points out... we need someone checking it.


predictive technology works well with known is the new unknown problems that haven't been encountered before that can't be predicted and may not be seen as a problem until we "teach" the machine what the problem looks like.

Level 11

Great article!

Level 13

Good post.  Thanks for sharing.  ML is a great aid in many areas, especially those where the humans get bored or fail to notice important anomalies or changes in the environment.


basically - don't be scared of new technology, learn about it, try it out for yourself, then decide if you will embrace it.

With this caveat.

If you decide not to embrace it and the rest of the world jumps on board you'll be left behind.

Not FOMO but as with all things tech, it is incumbent on yourself to learn new things.

My boss once said that if you aren't learning something new each year then you are becoming less and less valuable compared to your co-workers who ARE learning.


Thanks for the article.

Level 12

Does anyone have a good definition of machine learning? I have heard of machine learning for a long time but I have never seen a working example of actual machine learning. What I have seen is human applied automation of common tasks. The definition I saw includes "...that computer systems use to perform a specific task without using explicit instructions." While I might not be pushing a button every few minutes to tell the computer to compare A to B and based on the results do C, rather it might be done at a regularly scheduled time. To me that is an explicit instruction from me.

What I am trying to reconcile in my head is, is human applied automation of common tasks the same as machine learning. And if so is machine learning just cooler to say so that is what it is called?

Level 15

Thanks for the write up.

Level 13

Thanks for the article.  Good write up.


There are many methods and algorithms involved in machine learning.

It seems that for the most part you can break it down to 2 broad classes:

1) lets call these supervised algorithms where you provide some labeled data for learning (from a log file for example) where it can learn to distinguish normals messages from errors.

2) we will call these unsupervised algorithms where it can distinguish anomalous or abnormal messages without any previous training with labeled data.

either of those will require different approaches, study, and time to implement.

Level 12

thanks for the post