AIOps (Artificial Intelligence for IT Operations) is the “marketing term” that refers to the techniques and ideas associated with using machine learning based artificial intelligence to analyze machine data to automate the identification and resolution of common information technology (IT) issues.  AIOps offers IT some truly impressive improvements.


AIOps- automating to reduce MTTR and imrpvoe MTBF

There are three key benefits to AIOps

  1. Automating routine practices
  2. Improving the accuracy and time to identify issues.
  3. Improving the ability of teams to work together (because everyone can see the alerts at the same time)

It has proved surprisingly difficult for many vendors to deliver these abilities in a consistently cost-effective manner, often because the underlying technologies don’t have a consistent way of capturing machine data from all aspects of the application stack in a way that allows the value chain of the data to be maintained.

Data without context can be very hard for machine learning to analyze. Machine learning is about spotting patterns in the data, and identifying previously seen indicators of future issues.

The skill of a data scientist is to be able to describe the value of context of data to the algorithms, and that is a very time consuming (and thus expensive task).

The other choice is to find sources of data that already have contextual information included, this can significantly reduce the data science costs and time. This is the choice that Nastel has taken in our AIOps solutions. We abstract business context directly from the middleware messages that enterprise applications use to communicate. In this way we can automatically provide the machine learning algorithms with the meta-data they need to understand the reasoning about the business value of any given transaction. This allows for much faster time to value and significant reduction in false positives.

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