What Is AIOps? A Beginner’s Guide

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What is AIOps?

AIOps, also known as Artificial Intelligence for IT Operations, uses machine learning, big data, analytics, and other advanced technologies to identify and enhance IT operations like monitoring, management, service desk operations, and automation.

Today, systems within organizations consume and process massive amounts of data. Some companies experience millions of events over the course of a day, and manual processes are often tedious and which needs to be assisted through an automated system as the possibility of using a manual process for such use cases is questionable. AIOps is capable of identifying and reacting to such real-time issues more efficiently and proactively.

AIOps stands for Artificial Intelligence for IT Operations. It is a way to automate many of the tasks typically performed by IT administrators. It is typically used to automate the monitoring of IT infrastructure. The goal of AIOps is to use artificial intelligence to increase efficiency.

It is challenging to provide an outstanding user experience with no interruptions and high performance while dealing with diverse, complex and dynamic IT infrastructure with ordinary IT management options. Since AIOps can address this gap more effectively, experts state that AIOps will be the future trend in IT operational management.

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Why Do We Need AIOps?

The major reason why we need AIOps is that it could make the tasks of an ordinary Ops team more efficient. With AIOps, teams can provide more agile services to provide the optimum user experience. For a normal Ops team, this is a bit hard to achieve because there are normally a massive number of daily incidents to which the team needs to pay attention. Debugging and resolving these is a very complex process when the systems are quite large. Not only that but also there are multiple benefits that an AIOp can give you.

• Can remove noise and distractions - When the Op team has to occupy their time over some repetitive events, irrelevant warnings or alerts which are not critical, addressing such is a waste of time. AIOps can take care of those kinds of issues. They can be trained to detect such issues early and to resolve the issues that are impacting the services. Thus AIOps can speed up the whole system and increase the customer experience as well.
  
• Correlating the information to get the overall picture - When approaching an incident , it is more effective if you have an idea about the whole process and related entities. AIOps is capable of providing the holistic and overall big picture with the details of the interconnection of each entity of the system network; such as how the architecture is designed, what are the entities in the network,  applications, storage options and servers etc.
  
• Specialized direction to the issue - Another issue in a normal Op system is when an incident occurs,  it is hard to identify what team that the issue should be navigating to. One team may start handling the issue first, but later they might  figure out that the issue cannot be resolved under their boundaries. Then they have to direct it to somewhere else. It’s important to know the responsible section at this levelt. AIOps can help here by  facilitating a frictionless cross-team collaboration between different pastries and owners to diagnose and resolve the issues minimizing the burden that happens to the end-users.
  
• Benefitted by advanced machine learning concepts - We already know that AIOps runs on machine learning concepts. Machine learning is a rapidly evolving field. Therefore the training models and systems can continuously be improved with the help of the new concepts. Advanced and novel concepts that are used in AIOps can learn through the past and improve the performance to handle future occurrences.
  
• Knowledge reusing and root cause detection - Every resolved issue is a learning point for the AIOp model. Therefore the knowledge gained over time will be beneficial in future for the system to perform better. This procedure can take the Ops team very much closer to the ticketless and self-repairing environment.

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How Does AIOps Work?

Understanding how AIOps work is a lot easier when you identify the basic components that are used to implement AIOps and their roles in AIOps.The basic technical components used here are Big Data, Machine Learning and Automation. AIOps uses big data to aggregate operations data into one big picture. These data include:

• Historical data (events, incidents, performance analysis etc.)
• Transmission of real-time operation events
• System logs and metrics
• Network data
• Ticketing data ( Incident reports and events etc.)
• Other domain-related data

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Core Elements of an AIOps

AIOps is a highly emerging field. AIOps can have their own models to match with the business domain which are implemented using multiple of technologies.Therefore no AIOp models are the same. Even though many technologies and components  can differ from each other, the core of AIOps consist of the following elements :

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Observing IT data:

AIOps is based on the combination of data from IT Operations Management (ITOM) such as events, metrics etc. and data from IT Service Management (ITSM) like incident reports, and changes etc. While observing, It combines data from different tools so that they can communicate with each other to discover the root cause for the events efficiently or to enable automation. In other words, this is called "breaking data silos.". This process helps to make a strong unified environment to achieve productivity and improve accessibility.

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Data management:

With the transition to digital works, the evaluation of the data is now reaching its peak. Massive amounts of data are generated and consumed in each second. Therefore managing such an amount of data is very tedious and hectic for humans. In such a scenario a multi-model data lake is there to save you. Multi-model data lake acts as a repository for storing and configuring items, incidents, topologies, log data, performance data,  event details,  incident reports, events, releases,  deployments, errors and so on. Multi-model lakes can be used for predictive analytics too to have a more soothed data management flow.

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Anomaly Detection System:

With the aid of AI-based observability as discussed above, the system is intelligent enough to capture any unknown problems that are anomalies to the system. Anomaly Detection is a model based on advanced machine learning concepts. Anomaly detection provides a  pipeline to detect multivariate KPI anomalies and to log them. An anomaly detection engine can detect the anomalies in a system and then alert the responsible IT team. This mechanism can serve IT operators proactively by discovering problems and resolving them immediately avoiding them getting critical.

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Event Reduction System

With the extensive use of IT resources, a large number of events and incidents can appear on a system. There can also be false events. Manually identifying the proper events that occurred in a system with human power is a difficult task. The event reduction system uses machine learning models which are trained to easily identify any of the false positives in the system. This is a productive approach since it can cover up many new combinations which are not even noticeable for the people. In this way, IT operators can proactively change events and perform automatic repairs.

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Event / Incident Recognition

Event recognition is another function of the AIOps layer. This process can associate multiple KPIs and record the logs together to capture unidentified issues. Then perform root cause analysis to determine the actual cause of the problem.

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Interactive System

The most important part of an IT organization is the interaction between the users and customers in resolving service requests. In AIOps,  the AI-based model can handle all the  IT tickets, either a service request, incident report or any other issue. It can automatically add remedies or auto-fill to resolve the ticker. In this way, operators now only have to attend to deep and complex issues.

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AI-based Virtual Agents

AI-based virtual agents can automate all L1 helpdesk activities. Implementing virtual agents will be an added advantage to the organization because IT operators do not have to cover late-night issues to resolve every incident ticket manually. Simply we can say these virtual agents are an extended support team member,  who is available for 24x7 support.

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Cognitive Automation

We discussed how convenient it is to observe the IT system wIth the help of AIOps. Cognitive automation is the process of observing, identifying the anomalies and then automatically resolving them. AIOps can invoke alerts and recommended fic=xes to the teams automatically. It can also trigger automatic system notifications and responses to address issues in real-time even before the users got to identify them.

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Change Management System

With the machine learning capabilities of the change management system,  the organizations now can have thorough analytics over the historical data of events and modify their models to capture more problems with higher accuracy even earlier, Then they can plan the required changes, then evaluate the risks and how they are going to impact the existing process and entities,  then recommend and execute the changes to achieve better results. The beauty of AI models is they can help the system to learn and adapt to the changes happening in the environment. Even a new infrastructure revision or reconfiguration is done by the DevOps team is also now easily adaptable for the system.

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AIOps Use Cases

Digital transformation: with time, we could see most of the IT organizations are moving into digital transformation having multiple operational environments, dynamic infrastructures and using more virtual resources. In such scenarios, the complexity of the system increases. b the technical concepts being used in AIOps helps to tackle more scenarios easily. With AIOps, now the organizations have more flexibility to transform their business goals to the wider level without worrying over the IT operational complexities.

Intelligent alerting: AIOps can forecast how an issue or a failure is going to impact the rest by analysing the history data using machine learning and analytics. Therefore it can avoid raising unnecessary alerting from many entities just because of the same issue. For example, suppose that 10 systems are consuming a service offered by system B. If by any chance there is a reported issue regarding system B, there should be more than 10 alerts in other affected systems. With the help of Intelligent alerting, alert fatigue can be minimized and also issue prioritization can be done over the business impact.

Cloud-Native SMEs : AIOps can also be used in small and medium-sized enterprises (SMEs)that are needed to develop and release products continuously. AIOps allows them to tune up their digital services, preventing malfunctions, glitches and outages. Even for the organizations that are using hybrid multi-cloud environments, AIOps will help to capture the interdependencies and reduce the operational risks in such approaches.

DevOps adoption: As we discussed intensively, AIOps can provide the visibility that the IT team needs to support DevOps under a minimum management effort.

AIOps can be used in many more use cases including Anomaly detection, Root cause analysis, Automated future incident Prediction

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The Future of AIOps

There is rapid growth in the IT field with upcoming modern trends and technologies. As a result of that, we can predict that IT systems will go beyond the human scale sooner. Tooling and methodologies need to get upgraded to align with such scenarios. Therefore organizations will see AIOps as a great opportunity to overcome the challenges that are in front of them.

Most probably in the next few years, AIOps enables organizations can transform their organization with the following scenarios:

• Use of Analytics and orchestration to provide a more convenient and efficient uninterrupted user experience. This user experience will be automated or self-serving.
• Automation of business: costs lesser, increase the speed and accuracy, and preserving the human assets for higher-level tasks.
• More agility in DevOps / Enterprise ITOps with continuous delivery.
• Data to be the currency: Use of this high-end technology can open up more new doors to the business, AIOps can unleash high-level use cases and business growth opportunities.

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Conclusion

AIOps is now used in many places in the IT industry because of the tremendous opportunities and advantages that it gives to the business. The beauty of AIOps is that it allows innovation, increases volume and velocity while reducing the disruptors when handling a massive amount of data which can be a nightmare to humans. This does not imply AIOps can replace humans. Instead, AIOps just takes over the unnecessary burden over the people and makes them more utilized in the scenarios where an AI cannot perform well. After all, machines cannot cope without humans. a human should always be on top guiding and assisting the process to get the best out of the best.

If you are interested in Adopting AIOps and not sure where to start, we at Xperts can help you. Expert developers and PMs in Xperts can help you implement AIOps in your company. Our developers have experience developing managed applications in various fields including healthcare, education, and delivery systems. Get in touch with one of our experts today for a detailed quote!