54.png

AIOps brings about changes and improvements in IT operations with the aid of artificial intelligence and automation. It reduces human interaction thereby making IT operations faster and less error-prone. This article explores the critical areas you need to understand about the AIOps concept.


    AI is taking over. Thanks to its ability to approach problem-solving with human-like experience—a feat that cannot be achieved using traditional software. This ability has led to the vast adoption of AI in many industries where software solutions are applicable-even in software development.

    A typical example of AI usage in software development and delivery process is the relatively new kid in the block, AIOps.

    AIOps meaning Artificial intelligence for Operation is a term coined by Gartner in 2016 to define the use of artificial intelligence and machine learning models to directly or indirectly enhance IT operations.

    IT operations consist of a lot of repetitive tasks like monitoring, continuous integration, deployment and delivery. By training on massive data from previous and future scenarios, AIOps is able to improve the speed, efficiency and accuracy of such tasks without human intervention.

    A concept that is usually confused with AIOps is MLOps, both involving artificial intelligence/machine learning and IT operations. However, the key difference between the two concepts is that in AIOps, artificial intelligence, machine learning is applied to IT operations to improve the process. In contrast, MLOps refers to how IT operations and DevOps practices are used in machine learning model development. AIOps and MLOPs are direct opposites of one another.

    AIOps uses extensive IT diagnostic, artificial intelligence, predictive analysis, and automation executes and analyzes IT operations data and outputs intelligent diagnostics of issues in the IT environment. It also performs regulatory actions to ensure that the IT environment runs optimally without human interaction.

    To stare clear of running after the trend or just jumping on tech concepts, a great question to ask is, why do we need AIOps?

    You can understand this by getting to know some of the benefits and use cases of AIOps.

    Of all challenges faced in IT operations, IT noise and a slow problem resolution process appear to affect operations professionals' performance the most. IT operations personnel also face issues with monitoring their systems and getting quality analytics of the performance of their systems. These are some of the problems AIOps aim to solve.

    It is recorded that 72% of IT organizations rely on up to 9 different tools for modern applications data monitoring. AIOps eliminates the need to manage multiple tools for tasks like monitoring by tracking, collecting, and correlating events data from dynamic sources in the system and organizing them in pleasant view for further analysis without human intervention.

    The availability of comprehensive data from AIOps also improves the collaboration between different departments in the organization to achieve a common goal. With automation, AIOps also speeds up the problem-solving processes and reduces the costs spent on IT operations. Finally, AIOps improves user experience when using the organization's solution.

    However, organizations, companies, and software departments need to consider the state of their workflow before adopting AIOps.

    Users rely on software, now more than ever, to solve their day to day needs. As a result of this, there is more pressure on IT departments in organizations.

    To reduce this pressure, increase developer and IT personnel productivity, and improve the speed of deployment, agile software development practices such as DevOps needs to be in place before introducing AIOps. This will ensure that AIOps blends into the organization's workflow and achieves its goals.

    Many open-source AIOps tools and commercial AIOps platforms provide an easy solution to implementing AIOps in your workflow.  Some of the top open-source AIOps tools include Loglizer, Seldon Core, Log3c, and AIOpsTools, all providing primary log collection, anomaly detection, and insights for your IT environment. Dynatrace, Moogsoft, PagerDuty, and BigPanda are some top commercial AIOps platforms, providing advanced features than many of their open-source counterparts.

    You would love to read a similar article covering everything you need to know about AIOps to have a more detailed and uncut understanding of the concept.


    Get similar stories in your inbox weekly, for free



    Share this story with your friends
    editorial
    The Chief I/O

    The team behind this website. We help IT leaders, decision-makers and IT professionals understand topics like Distributed Computing, AIOps & Cloud Native

    Latest stories


    DevOps: Report on Devil's Practices by DORA

    The report is drafted from a report release of the annual research and survey of …

    Amazon Elasticsearch Gets a New Version With Name Deprecated

    Accompanied by new advancements is Amazon OpenSearch, the same body of code as its predecessor, …

    McAfee Partners With IBM Security to Deliver TD Synnex Security Solution

    The MVISION platform and Security wing of IBM's partnership endgame are to extend increased protection …

    Amazon MSK Connect Launched to Better Apache Kafka UX

    Amazon follows up on its 2018 data streaming software, Amazon Managed Streaming for Apache Kafka, …

    Cloud: Zone Redundant Storage Released on General Availability

    The report is drafted from a press release of the Microsoft Azure team on the …

    Security: IBM Traces Two-Thirds of Compromises to Misconfigured APIs

    The report is drafted from a sweeping survey of dark web analysis and various X-Force …