Recently I came across one of the very interesting topics- AIOps.
AIOps refers to Artificial Intelligence for IT operations. It involves the use of machine learning, big data analytics, and AI tools to automate the IT infrastructure of an organization.
In larger enterprises, the applications, systems, and services generate massive data volumes. With AIOps, organizations can utilize this data for monitoring their assets and examining their IT dependence more closely.
Ideally, an AIOps solution provides the following functionalities.
1- Automation for Routine Procedures
AIOps facilitates organizations to integrate automation in daily routine procedures. This automation can be performed for requests from users or to manage non-critical notifications from the system. For instance, if AIOps is used, then a help desk system can respond appropriately to a request from a user—all by itself. Similarly, AIOps tools can assess an alert from a system and evaluate if it requires any action without the need of a supervising authority.
AIOps can detect critical issues quicker and better than any manual strategy. When a familiar malware is detected on a non-critical system, the IT experts may try to eliminate it. In the meantime, they might miss an unusual activity or process on a critical system from a newly-arrived and sophisticated threat. As a consequence, the organization suffers a huge setback.
On the other hand, AIOps can make a difference by the use of vulnerability prioritization .i.e. it immediately notifies the authority about a possible cyber invasion for the critical system while for the non-critical system, it can respond by running an anti-malware tool.
3- Streamlining Interactions
Historically, before AIOps was in the scene, teams had to share and work on information through either meetings or exchanging data manually. AIOps can streamline the communication and coordination processes between teams and data center groups. It shows “relevant” data to all the IT groups. For this, the AIOps platform must be designed in a way so that it can monitor and analyze which type of data to present to which of the team.
AIOps combines several techniques for aggregation, algorithms, analytics, data output, visualization, machine learning, and automation and orchestration. All of these techniques are mature and integrated. So how do they work?
Log files, helpdesk ticketing systems, and monitoring provide data for AIOps. Then big data tools are used to properly manage and aggregate any data coming from the system as an output and convert it into a more useful format. To do this, analytics methods and procedures are used which attempt to extract raw data and transform it into a meaningful fresh piece of data. Therefore, analytics eliminates “noise” and irrelevant data. Additionally, it also searches for recurring patterns that can detect and mark common issues.
However, analytics cannot run without the use of proper algorithms. Algorithms support an AIOps solution to respond with the most appropriate course of action. Algorithms are configured to ensure that the IT staff can help the platform learn about the decisions pertaining to the application performance.
Algorithms are the center of machine learning. The AIOps platform sets out a standard for normal activities and behavior where it can continue to update by adding new algorithms with the addition of new data in the infrastructure of the organization.
Automation ensures that any AIOps tool is quick in performing the required action or set of actions. Automated tools are “forced” to act based on their communication with machine learning and analytics tools. For instance, a machine learning tool may establish that an application in a system requires additional storage to function. This piece of information is passed out to an automation tool which resolves to perform an action like adding more storage.
Lastly, to help in decision making, visualization is used in AIOps. It generates dashboards which are extremely easy to use and read. These dashboards contain graphical representations of all kinds, reports, and other visual elements to simplify different types of output. As a result, the management is able to remain in the loop and take any rational decision.
How Has AIOps Proved to Be a Breakthrough?
Before the emergence of AIOps, organizations faced difficulties because their IT personnel spent much of their time on routine and basic tasks. AIOps proved to be a breakthrough by helping organizations focus on more critical issues. As a result, such platforms have saved a great deal of time. IT personnel now attempt to train and educate AIOps platforms to become familiar with the organization’s IT infrastructure.
Afterward, it continues to update and evolve by making use of machine learning and algorithms as well as going through the “learned” history which it accumulated with the passage of time. Therefore, they provide for an excellent monitoring tool that has the “rationality” to perform many useful tasks.
Moreover, AIOps platforms examine and inspect causal relationships from various services, resources, data sources, and systems. Machine learning functionalities identify and run robust root cause analysis. As a result, troubleshooting of frequent issues is enhanced.
Furthermore, AIOps assists organizations to increase collaboration among all the departments. With the reports from visualization, team leaders are able to comprehend requirements and perform their duties with a renewed sense of direction.
The Other Side of the Coin
AIOps is extremely promising, but some analysts consider it to be unrefined. The debate that the effectiveness of an AIOps platform is as powerful as its “training” while the time needed to create, implement, and administer such a platform may be too time-consuming for many organizations.
Likewise, they argue that due to its ability to perform a wide variety of tasks, it requires trust from organizations. Since AIOps tool works autonomously, they have to be trained in such a way that they can easily adapt according to the environment of their organization and be able to accumulate and collect data, come up to the most logical conclusion, and allocate actions accordingly.