December 8, 2022
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Machine Learning Aided Incident Management In an Enterprise – Digital Journal

“Machine Learning | Incident Management | MIRAT.AI”

Enterprise IT organizations can achieve their goals of proactively identifying emerging issues and preventing incidents by utilizing AI and machine learning capabilities and solutions.

Automated processes and operations can reduce human error in a wide range of business activities. The sheer volume of data generated by today’s complex IT organizations makes it impossible for humans to sift through, organize, and analyze the data in order to determine which data is meaningful and how it informs their processes and decisions. 

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When it comes to data analysis, however, Machine Learning is a far more powerful tool than any human could ever be. Machine learning can help IT organizations improve their DevOps processes and be more proactive about service change so that they can deliver value. 

AI tools like Machine Learning and Natural Language Processing can be used by organizations to implement an Enterprise Incident Management strategy. A proactive approach to incident management will be discussed as a means of improving an organization’s adaptability. 

Prevention is influenced by a number of service-related factors. 

IT organizations can achieve their goals of detecting emerging problems and proactively preventing problems with AI and machine learning capabilities and solutions.

Implementing a Service Impact Prevention strategy requires the following three components:

1. Utilize artificial intelligence to discover new problems

If you have a large amount of data, you can use machine learning tools to mine it and identify emerging issues before they become incidents. Natural language processing (NLP) and machine learning, for example, can mine data from service reports and incidents to identify key themes and topics as well as complete root cause analysis.

It is possible to use machine learning to identify common risk factors and separate them from data that is unrelated. Analyzing data trends, patterns, and combinations can help identify which data is a risk indicator or a precursor to an emerging risk or pattern and which data is not. 

2. Keep an eye out for potentially dangerous situations

A major incident can be predicted using machine learning, which can identify which combinations of risk factors are most likely to result in an incident of this magnitude. ML, for example, can locate meaningful data combinations by identifying unusual …….


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