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How Machine Learning Can Help IT Managers Get Out Of The Mud Of Operations And Maintenance

- Apr 19, 2018 -

How machine learning can help IT managers get out of the mud of operations and maintenance

Introduction: In recent years, the application of machine learning technology in monitoring tools has become a hot topic for IT operations and DevOps team.

The traditional operation and maintenance methods basically rely on artificial and static rules, and they cannot adapt to dynamic and complex changes. Artificial intelligence can provide the ability of operation and maintenance with machine learning and algorithm, so that it can make efficient and accurate decision making under the complex conditions of dynamic change scenarios. We need to have a shift from "based on expert experience" to "based on machine learning."

In recent years, the application of machine learning technology in monitoring tools has become a hot topic for IT operations and DevOps team. Although there are many related use cases, the real "killer application" for the IT team is how machine learning can improve real-time event management capabilities, thereby helping larger enterprises to improve service quality. In this regard, the key is to detect anomalies earlier before the user finds out the problem, thereby reducing the negative effects of production accidents and interruptions.

 

In the course of operation and maintenance, a large amount of operational and maintenance data is generated. Some of these can be used to describe the application or system operating status, some can be used for labels, and some can be used for empirical feedback. These massive, multi-dimensional data are the basis for machine learning to establish behavioral models.

What are the specific advantages? First of all, the advantage of machine learning is that it can be customized through unsupervised learning to meet the company's unique business environment. Machine learning achieves this advantage by adopting various algorithms that identify consistent, coherent, and cyclical patterns of data that can be practically applied to business activities, challenges, and opportunities.

 

In addition, today's companies often have a large amount of data, but most of them are not used or available, and may be rapidly changing. These data are too large. Even the analysts of the entire force cannot expect to be fully controlled. With machine learning, the advantages of big data can be effectively achieved by embedding operational intelligence into existing performance management tools. For example, assuming a large department store uses machine learning to analyze sales transactions, it can easily evaluate billions of transactions and related metadata and derive valuable information from them. This information can be incorporated into existing tools to help the store improve its internal operations and improve the end-to-end customer experience.

Not only that, machine learning can also help make up for the gap left by IT operations experts when they retire or leave the company. For example, a new generation of IT experts may not have been trained in mainframe technology, and many leading companies and governments rely on this technology to execute their most important applications. Embedding smart and applied machine learning technology, which incorporates the skills and knowledge of mainframe experts, can reduce risks and ensure that organizations can achieve continuous and scalable operations, thus making up for the lack of expertise in optimizing mainframe performance and troubleshooting.

 

Of course, this does not mean that enterprise IT operations can seamlessly move closer to machine learning. In fact, machine learning can be divided into two phases of application. The first stage is to link data from different IT tools. The second stage is to determine where the association is the most meaningful. In the first phase of dealing with unstructured data, the process of contact is not obvious.

Machine learning can infer the relationships between different data sources and determine how they can be linked to the relevant operating environment. Algorithms include fuzzy matching rules and how to identify association rules for events that occur at the same time, data language analysis in natural language, and estimation systems based on prediction models. In the process, a series of cross-data semantically labeled data samples were generated.

The development of IT operations and maintenance is sufficient to make all automation functions automatic and use sophisticated component tools to ensure everything is working properly. IT operation and maintenance analysis has entered a new era - an area where algorithms deal with IT operations and maintenance. The process of learning algorithms is integrated into the collection of large amounts of data, alerts, tickets and measurements to extract them. Insight, this insight will be able to provide accurate alerts, establish awareness of situational awareness, find root causes, and even predict events