A Guide to Artificial Intelligence for IT Operations (AIOps)

Many industries in the world have adapted and used Artificial Intelligence in many ways. Industries like finance, healthcare, IT, business, and many others use AI actively. 

 

Talking about IT industries, it has become quite essential to monitor and manage modern IT environments that are hybrid, dynamic, distributed, and componentized. Because conventional domain-centric monitoring and IT operations management can't connect the flood of data generated by multiple IT domains, traditional, domain-centric monitoring, and IT operations management are becoming obsolete. (Also learn how AI changes everyday life)

 

So they are unable to offer the information that IT operations teams require to manage their environments proactively. This is where AIOps come to play.  The term AIOps was first coined by Gartner. Now the question arises, what exactly is AIOps? In this article, we are going to discuss AIOps.

 

What is AIOps?

 

AIOps is the short term for Artificial Intelligence for IT operations and it is an application of advanced analytics in the field of Artificial Intelligence and Machine learning to solve IT operations problems. It combines big data with Machine learning to procure predictive results that might help drive quicker Root-cause Analysis (RCA) and accelerate Mean Time to Repair (MTTR). 

 

With the help of algorithmic analysis of IT data, AIOps enables IT Ops and DevOps teams to operate smarter and quicker, allowing them to discover and fix digital-service issues sooner, before they have a negative impact on business operations and consumers. 

 

Ops teams may use AIOps to manage the enormous complexity and volume of data created by modern IT infrastructures, preventing failures, maintaining uptime, and achieving continuous service assurance.

 

(Related blog: DevOps vs MLOps)

 

Working of AIOps:

 

The different AIOps products are created differently and work differently. A company should install it as an independent platform that consumes data from all IT monitoring sources and functions as a central system of engagement to get the most value out of it. 

 

Five distinct algorithms are used to totally automate and simplify five essential aspects of IT operations monitoring according to moogsoft.

 

1. Taking in huge amounts of meaningless and noisy IT data that is generated by the modern IT industry and filtering the data to select the data elements that might create any problem.

2. Finding any existing relationship between the meaningful data elements that are sorted out and putting them into groups for future analysis.

3. Identifying the underlying causes of difficulties and reoccurring issues so that one may act on what they've learned.

4. Notifying the right operators and teams, as well as allowing cooperation among them, especially when personnel is geographically separated, as well as storing data on events that might help speed up the future diagnosis of similar issues.

5. Automating the responses as much as possible in order to make quick and precise results.

 

(Read also: Machine learning operations (MLOps))

 

Advantages of AIOps:

 

AIOps provides speed and precision to the Ops team of any IT sector that they were lacking before. It not only makes the manual work easier but also ensures the uptime of critical services and a better customer experience. The other benefits of AIOps are:

 

1. AIOps eliminates noise and distractions, allowing busy IT professionals to focus on what matters most rather than being distracted by unnecessary notifications. This reduces the time it takes to discover and resolve service-impacting issues, as well as the number of interruptions that negatively impact sales and the customer experience.

2. AIOps removes boundaries by connecting data from numerous sources and providing a clear, contextualized view of the complete IT environment – infrastructure, network, applications, and storage – both on-premises and in the cloud.

3. AIOps shortens evaluation and resolution periods for individual customers by allowing seamless cross-team cooperation between diverse professionals and service owners.

4. Advanced machine learning collects relevant data in the background and makes it available in context to enhance future situation management.

5. The procedures for fixing recurrent issues may be automated via knowledge recycling and root cause analysis, bringing Ops teams closer to a ticketless and self-healing environment.

 

(Suggested blog: Machine Learning algorithms)

 

Elements of AIOps:

 

AIOps is considered as Continuous Integration and deployment (CI/CD) for core IT functions. There are three different disciplines of IT that are followed by AIOps including Service Management (Engage), Performance Management (Observe), and Automation (Act). Given below is Gartner’s diagram that visualizes the AIOps platform.

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Gartner’s visualization of the AIOps platform (source)

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According to bmc blogs, the following are the key elements and their contributions that are essential for AIOps:

 

1. Extensive and diverse IT data:

 

AIOps is based on combining data from both IT operations management (ITOM) (metrics, events, and so on) and IT service management (ITSM) (incidents, changes, etc.). It brings data from separate technologies together so they can "speak" to one another or enhance root cause discovery or allow automation, and is referred to as "breaking down data silos."

 

 

2. Big Data:

 

Data must be brought together as it gets released from segregated tools to support next-level analytics. This must happen not only in the background, as in a forensic inquiry utilizing previous data, but also in real-time as data is absorbed.

 

 

3. Machine Learning:

 

Big data permits the use of machine learning to evaluate large amounts of disparate data. This isn't feasible before putting the data together, and it's also not achievable with manual human effort. ML automates manual analyses and allows for new analytics on fresh data, all at a scale and pace that would be impossible to do without AIOps.

 

 

4. Observe:

 

This is the evolution of the conventional ITOM domain, which now includes development as well as non-ITOM data (topology, business indicators) to enable additional correlation and contextualization methods. When combined with real-time processing, probable-cause detection, and issue creation become one and the same.

 

 

5. Engage:

 

Bi-directional connection with ITOM data to enable the aforementioned analyses and auto-create documentation for audit and compliance/regulatory needs is part of the conventional ITSM domain's development. Cognitive categorization, routing, and intelligence at the user touchpoint, such as chatbots, are examples of how AI/ML manifests itself here.

 

 

6. Act:

 

This is the AIOps value chain's "last mile." If responsibility for action is returned to human hands, automating analysis, process, and documentation will be for nothing. Act entails the codification of human domain knowledge into remediation and response automation and orchestration.

 

(Must catch: AI Algorithms/ models)

 

AIOps Users:

 

AIOps is used by many organizations and industries that involve IT operations. Some of them are listed below:

 

1. Successful businesses:

 

There are many companies with extensive IT environments and spanning multiple technology types that are facing complexity issues. When these factors are combined with a business strategy that is largely reliant on technology, AIOps may make a significant impact on the company's performance. 

 

Despite the fact that these companies operate in a variety of industries, they all have a similar scale and a quick and rising rate of change, as the requirement for business agility drives up demand for IT agility.

 

(Read also: how AI helps in business)

 

 

2. SMEs:

 

Small and medium-sized businesses (SMEs) are also adopting AIOps, particularly those that were established in the cloud and need to create and deploy software often. These cloud-first SMEs can constantly improve their digital services while avoiding faults, malfunctions, and outages using AIOps.

 

 

3. DevOps teams:

 

Companies that use a DevOps approach may find it difficult to maintain alignment among the many roles involved. Direct integration of development and operations systems into a larger AIOps model eliminates a lot of the possible friction. 

 

DevOps pipelines also create a large quantity of data. DevOps executives must examine application delivery rapidly and frequently to ensure its stability and speed.

 

(Must read: AI in cybersecurity)

 

 

4. Hybrid cloud using organizations:

 

While there are several advantages to moving workloads to a public cloud platform, there are also compelling reasons to maintain some apps and equipment on-premises. 

 

As a result, many businesses are forced to operate in hybrid settings, which presents its own set of IT operations problems. 

 

AIOps helps Ops teams retain control over these environments and offer service assurance by providing a holistic, complete picture across all infrastructure types and assisting operators in understanding relationships that change too rapidly to be recorded.

 

 

5. Companies undergoing a digital transformation:

 

The digitalization of corporate operations in order to make a company more efficient, flexible, and competitive is known as digital transformation. IT is at the core of digital transformation projects, and it must function at the speed that the company requires in order to avoid becoming a bottleneck that obstructs the fulfillment of the larger objectives. 

 

AIOps helps IT offer the degree of technical support that successful digital transformation initiatives demand by automating IT operations and eliminating errors that impair these digitized processes.

 

Conclusion

 

AIOps is used in many places now and has benefited businesses tremendously. Not only does it increase the agility and precision of the process but it is also connected directly with customer experience. AIOps allows innovation while fending against disruptors and manages the volume, velocity, and diversity of digital data that is too large for humans to handle. But, it should not be misinterpreted as AIOps replacing humans, as this is not the case. 
 

AIOps on a larger scale than humans does not imply that people are being replaced by computers. To deal with the new reality, we'll need big data, AI/ML, and automation. Although humans will not be replaced, AIOps workers will need to learn new abilities.