"And now please park in reverse". Drivers who are beaded with sweat will be grateful for this invention: automatic parking assistants manoeuvre the vehicle into a parking space - and out again if necessary. Completely self-driving cars have long been a vision of the future, but now they will soon be roadworthy. At the end of 2021 Mercedes-Benz as the first vehicle manufacturer to receive so-called "system approval" for highly automated driving (Level 3). This means that the driver no longer has to permanently monitor the vehicle. For some time now, functions such as Lane Assistant, distance control and automatic parking have been supporting drivers in their everyday lives. We are thus benefiting significantly from AIOps - Artificial Intelligence for IT Operations.

In which areas are they particularly used, how can companies benefit and how do AIOps actually work?

1. what is AIOps?

The term AIOps comes from 2014 and was coined by the US market research company Gartner. They describe AIOps as tools in which artificial intelligence, machine learning and big data contribute to the optimisation of IT processes.

Artificial intelligences evaluate data from integrated systems, such as a car camera, and derive actions from it. In addition, they are constantly learning and thus reduce the need for IT staff. Internal processes can thus be significantly reduced, which can contribute to relieving the internal structures. AIOps can therefore be used to work with large amounts of data in a resource-saving way.

AIOps is not to be confused with MLOps. Although the terms sound quite similar, there are quite big differences between the two disciplines. MLOps - unlike AIOps - is not a machine learning functionality per se, where data is processed by algorithms. Rather, it is a way to manage and optimise the development, deployment and maintenance of these algorithms.

2. how do AIOPs work?

Machine learning is an aspect that underlies artificial intelligence, enabling it to recognise patterns from data without requiring specific programming. AIOps make it possible to apply Big Data and machine learning to IT systems on a large scale. Possible Areas of application are:

• Automations
• Monitoring
• Service Desk

It is important to distinguish between two different types of algorithms in use:

• Supervised algorithms work with raw data that are classified using examples. This results in predictions and calculations for the future. For example, algorithms can recognise people if they have been fed other images of the person in question. Another example would be the recognition of suitable applicants for an advertised job.
• Unsupervised algorithms work exclusively with raw data, which can be clustered into groups by AI. The algorithm recognises different patterns, which are then sorted and assigned. The algorithm is not provided with any examples or labels at the beginning. Unsupervised algorithms make decisions that cannot subsequently be understood or explained by humans. This is a big problem because decisions have to be explained to customers or companies in order to maintain transparency and control. This raises the question of responsibility and legal consequences.

3 Why do companies need AIOps?

Due to increasing internal complexities, the ever more diverse IT world needs overarching approaches such as AIOPs that offer efficient solutions. They are essential for autonomous and reactive functions based on large amounts of provided data. They filter important information or problems from large amounts of data and solve them independently through automation. In this way, AIOps on the one hand relieves IT departments and at the same time ensures that the large flood of data does not lead to a paralysis of existing systems.

AIOps supports IT areas without increasing effort and facilitates the cross-cloud integration of data. To do this, AIOPs proceed as follows:

• Scanning environment: Large amounts of data are collected across systems and structures. This can be in various forms, depending on which systems the AI has access to. A good example is visual data such as video footage.
• Detection of anomalies: Data is intelligently filtered and problems or special occurrences are detected. The AI thus detects abnormalities in the video footage based on colour changes, speed or movement.
• Independent analysis and reaction: There is an analysis of possible errors and their causes as well as an appropriate reaction or conclusion. The resulting clusters lead to assumptions that generate a response for action.
• Correction of anomalies and errors: If an anomaly is detected, the AIOp goes into independent remediation of problems and their cause. These functions are based on artificial intelligence algorithms that recognise independent patterns and thus act without an administrator. For this purpose, data is evaluated and conclusions are drawn accordingly. If problems occur, the artificial intelligence is able to initiate appropriate solution processes. If errors persist, warning messages and alarms are sent, which must be addressed manually.

In which areas can companies that use AIOps in their IT management benefit most? The benefits are multi-faceted:

• Real-time response and problem solving: By using artificial intelligence, problems and anomalies can be detected at an early stage and without human intervention. These can be production errors, for example.
• Work reduction for the IT department: Since AIOps take care of large amounts of data, the workload of the respective IT department is greatly reduced. The manual effort that is eliminated at this point can be used profitably in other places.
• Cost reduction: The relief of the IT department and the avoidance of errors leads to lower financial expenses.
• Independent optimisation: With the use of AIOps, companies are well prepared for future problems, as machine learning and AI evolve with their data and learn as they go.
• Overview: AIOps bring together data across the board and thus facilitate the handling of complicated IT landscapes.

6. application examples from practice

AIOps can be found hidden in our everyday lives. A prominent example close to people are the latest technologies in the automotive industry. Cars today are equipped with a multitude of sensors and cameras that constantly collect information and work edge to cloud. Only through the use of AIOps is it possible to turn this data into useful technical tools. In this way, a car can automatically keep in lane, measure distances, initiate a braking function or keep an eye on the blind spot.

These functions are based on AIOps. To guarantee safety on the road, they must reliably scan, assess and react to the situation in real time. In this way, human error can be prevented and more road safety can be promoted. But AIOps are also used in other sectors such as mechanical engineering: In the context of smart factories, machines, robots and co. are monitored and controlled automatically. AIOps can also be used in this context to supplement the functions of SAP systems.

7. we take a look into the future

The Gartner Market Guide for AIOps Platforms 2021 says: "There is no future of IT operations that does not include AIOps." The reason for this is the aforementioned rapid growth in data volumes and the pace of change. To deal with this data effectively, more and more AIOps will be used in the future, because in a cloud-based world they are a comprehensive catalyst. Through data filtering and data analytics, AIs are a big part of future solutions. Gartner also concludes that the rapid growth of data volumes means that companies can no longer wait for humans to derive insights from data.

In the future, it will be crucial that companies build a good infrastructure that provides optimised information to AIs in order to grow with them. Especially in areas such as infrastructure or monitoring technologies, AIOps can help to deal with large amounts of data in a resource-saving way. It can therefore be assumed that smart artificial intelligences will continue to prove their added value in the future and replace ITOps in the long term.