What is AIOps? Why it’s So Critical, and How to Get Started

    AIOps stands for “artificial intelligence for IT operations.” At its core, AIOps is all about how IT organizations use AI to manage data in their environments. Through this approach, teams can employ large-scale data sets, machine learning, and automation to make IT operations faster, simpler, and more efficient. 

    In this post, I’ve offered a detailed introduction, offering answers to these questions about AIOps:

    Why is AIOps important?

    AIOps enables IT operations teams to take full advantage of modern AI to improve visibility into IT systems, as well as to automate many operations processes. Instead of having to rely on IT engineers to identify a problem with an application and fix it manually, for example, a platform can use algorithms to identify and resolve the problem automatically. Likewise, rather than requiring IT staff to determine how best to manage application performance or how many resources to allocate to it, a platform can provision environments automatically by parsing data to determine the optimal mix of resources.

    What problems is AIOps intended to solve?

    IT teams play an integral role in advancing the most critical digital transformation imperatives. For today’s businesses, there’s a premium on delivering optimized user experiences—all the time and every time. While managing IT operations so they deliver optimized service levels and user experiences is vital, it’s also increasingly challenging. That’s because teams are dealing with:

    • Too much complexity. Most enterprise-class business services now rely not only on traditional systems, including on-premises mainframes and distributed systems, but on a plethora of new, dynamic technologies, such as containers, cloud delivery models, virtual and software-defined components, and more.
    • Too many tools. As more technologies were added to the mix, so were new tools. Working with dozens of tools, teams now struggle with hundreds of thousands of alerts that feature a high rate of inaccuracy and redundancy. Lacking unified visibility that spans their hybrid environments, staff have to take too long to inspect various systems and domains in order to identify the root cause of issues. As a result, customer experience suffers while triage calls run for hours. 
    • Too much rapid change. In recent years, organizations have started to deploy containerized applications on a large scale. The switch from conventional application hosting technologies (like virtual machines) to containers has made application environments significantly more dynamic, with individual container instances spinning up and down constantly. Like containers, microservices applications are fundamentally more complex than their predecessors because they consist of multiple services, each starting and stopping at different times. 
    • Too much data. The volume, variety, and velocity of data that needs to be managed, correlated, and analyzed continues to grow dramatically. In the wake of initiatives like multi-cloud deployments, microservices development, and Internet of Things (IoT) implementations, teams continue to see explosive growth in the operational data being generated. Ultimately, internal team members simply can’t keep pace.

    To contend with all the challenges outlined above, IT teams can’t simply try to do the same things better. To ensure their complex, hybrid, interrelated, and highly dynamic environments deliver an optimized user experience, operations teams have to achieve fundamental breakthroughs in scale and efficiency.

    It’s no longer enough to just react a little faster when issues arise. Teams must gain the visibility needed to identify potential issues—and address them before they affect service levels. To contend with the explosive growth in data, complexity, and user demands, IT teams need to adopt an AIOps platform. 

    How is AIOps used?

    When teams successfully employ AIOps technologies and approaches, they can establish the following capabilities. 

    Predictive Identification of Potential Risks to Services 

    Leveraging traditional, reactive monitoring tools and approaches, IT teams lack the insights needed to effectively predict issues before a business service or application is disrupted. Given the criticality of delivering a phenomenal user experience, these teams need a platform that offers algorithmic- or AI-based insights for detecting abnormal behaviors, predicting potential issues, and enabling real-time response. 

    It is also essential that platforms offer capabilities for mapping issues to associated services, so IT teams can intelligently prioritize troubleshooting and remediation efforts based on which issues will have the biggest potential business impact. For example, if two issues arise and administrators can see that one is affecting a payroll service that isn’t being run currently, and another is hitting an e-commerce service that runs 24/7 and accounts for the bulk of the company’s revenues, they can prioritize their efforts accordingly. 

    Automate Root Cause Analysis Across Domains and Technologies

    Even with the best predictive tools in place, downtime and application performance issues may still arise, whether due to an administrator’s configuration error, external service outages, or a host of other causes. 

    Within many IT organizations, when these application performance issues or outages occur, operators struggle to determine why. While a single issue may be the culprit, large numbers of redundant or false alerts may be generated, making it difficult for administrators to filter through the noise and identify the issue that needs to be addressed. At the same time, when operators see that a particular service is experiencing issues, it may be difficult to determine how or if the issue is affecting business services.

    To combat these challenges, operators need timely, targeted insights that can enable fast, automated analysis of the root cause of issues. To address these requirements, platforms need to provide machine-learning-driven intelligence that can automatically identify the probable cause. To support this intelligence, these platforms must also offer a topology analytics service that automatically discovers and maps key IT assets and stores topology information in a graphic database. This service needs to consume big data and correlate real-time intelligence from multiple architectural layers to effectively determine the probable cause. 

    Establish Comprehensive, Contextual Automated Remediation 

    Once an issue has been identified, whether predictively or through automated analysis, IT teams need comprehensive, intelligent capabilities that can automatically execute the remediation tasks required. To ensure success, platforms need to provide scalable, flexible, and easy-to-use automation that can be aligned with complex, dynamic enterprise IT environments and rapidly evolving business requirements. 

    AIOps platforms must be able to orchestrate the delivery of services in business, application, and infrastructure layers, across on-premises, cloud, and hybrid environments. Further, this automation should seamlessly support complex, organization-specific processes. For example, a platform may detect an impending issue that requires an additional AWS EC2 instance to be provisioned. This server provisioning may need an approval or system check, for example from a budgetary, compliance, or business perspective. These types of approval workflows should be easily accommodated. By leveraging these capabilities, IT teams can ensure that service requests aren't just logged, they are acted upon in real time—before the end user has a negative experience. 

    What Are the Key Requirements of an Effective Solution?

    With AIOps platforms, teams can leverage AI-based algorithms to predict potential issues that could affect service levels, perform automated analysis, and quickly run effective remediation across diverse, hybrid environments. Following are the key requirements for establishing effective capabilities. 

    Dynamic, Policy-Based Configuration

    Today’s environments are too dynamic to rely on cumbersome configuration efforts. To support agile and DevOps approaches, monitoring needs to be deployed quickly. When teams need to deploy performance monitoring for a new infrastructure technology or application, the process should be a low-touch, automated, and turn-key exercise.

    In today’s environments, time to monitor is a critical metric for IT teams to track and improve. Through dynamic, policy-based configuration, IT teams can make significant gains in reducing time-to-monitor metrics. By implementing a standardized, automated approach to infrastructure and performance monitoring deployments, IT organizations can better support rapid deployments and optimize service levels.

    Tools should provide dynamic discovery capabilities, so as new systems or services come online, they can be automatically detected and have appropriate monitoring configurations applied. These discovery capabilities should have the intelligence to determine the resource and type of technology, no matter the environment. For example, in the cloud, the platform should be able to discover and distinguish between a new EC2 instance and RDS, or an Apache server versus SQL Server. This approach also reduces technology-specific complexities and enables staff to adopt new technologies faster. 

    At the same time, tools should offer the ability to consistently enforce controls. Templates should be available that enable both consistency and efficiency in the application of monitoring configurations for similar resources. Tools should provide templates that have been developed based on industry best practices for each specific technology and environment.

    These dynamic platforms also need to feature capabilities for intelligent alarm configuration, enabling dynamic baselines and thresholds that help reduce false alarms.  

    Unified Monitoring and Analytics

    IT teams need a single view into their entire environment. Whether they are running traditional infrastructures, containers, hyperconverged infrastructures, multiple clouds, or all of the above, they need a unified way to monitor it all. They should be able to get proactive, actionable insights and correlation across various elements.

    To meet their mandates of ensuring optimized user experiences, IT teams need a unified solution that delivers out-of-the-box coverage of the entire IT infrastructure, whether the environment is comprised of on-premises platforms, cloud services, or a hybrid combination of both.  They should also be to get comprehensive coverage, not only of their environments, but of the various data types that are needed, including performance, availability, anomalies, and log data.

    Tools should also offer an interface that enables intuitive, flexible capabilities for slicing big data in different ways, including by specific applications, infrastructure elements, development and test environments, and more.

    With these capabilities, IT organizations can realize a number of benefits. Instead of being relegated to lengthy all-hands triage calls and finger pointing associated with having different infrastructure or application teams working with their own tools, organizations can boost team productivity and collaboration. In addition, IT teams can speed resolution when issues arise, and gain the predictive insights they need to address potential problems before they have any impact on the user experience. 

    Intelligent Automation

    Today’s IT teams are under constant pressure, facing demands to do more with less, while at the same time ensuring service levels are optimized at all times. To respond, IT teams must make automation a key pillar of their infrastructure management and performance monitoring strategy. 

    Automated discovery of new systems as well as automated deployment of monitoring of these new elements is vital in order to support today’s dynamic infrastructures and DevOps environments. IT teams also need to ensure dashboards and reports can automatically be adapted to evolving environments, and automatically generated and refreshed. 

    IT teams will also need to leverage capabilities for automated response and remediation workflows. For example, by leveraging integration with automation tools in AWS EC2 environments, monitoring solutions can identify potential capacity bottlenecks and automatically provision a new server.  

    Finally, to be successful, teams need to establish intelligent automation that spans complex workflows and multiple domains. By coupling intelligent automation with AI, AIOps platforms will enable teams to begin to establish self-healing environments. 

    Extensibility 

    As technological innovation continues to accelerate and requirements continue to evolve, IT teams need to be able to respond with maximum agility and speed. It is critical that monitoring solutions effectively support these objectives. 

    Tools need to support a wide range of technologies and environments. In addition, as new systems are employed, the process of establishing monitoring coverage of these new technologies needs to be fast and easy, whether through APIs or intuitive, wizard-based interfaces. Monitoring products also need to support easy and efficient integration with other IT operations management tools, including service desk platforms, DevOps management tools, network monitoring platforms, and more. 

    How do you get started with AIOps?

    As they set out to establish their implementations, enterprise IT teams have a number of choices, including whether to leverage commercial offerings or build their own capabilities using open-source technologies. No matter which approach is employed, there are three key pillars upon which a successful AIOps implementation is based:

    • Establish a unified, comprehensive data lake. It’s essential to establish a data lake that ingests and stores a wide range of data sets and data types. Platforms should support topological data, alarm metrics, log files, configuration management databases, and more. These different, disparate data sets need to be normalized and correlated.
    • Leverage the right algorithms. Teams don’t need to reinvent the wheel. The reality is that the algorithms required have existed for some time. The key is knowing which algorithm to use at which time.
    • Ask the right questions. Throughout the process, it’s critical to have the right questions in mind and ensure that the platform delivers the intelligence needed to answer the questions that matter. What’s the optimal mix of cloud and on-premises resources? What workloads should get migrated to a cloud environment? How do issues get identified and preempted before users ever experience a hiccup? Effective implementations can yield powerful insights into these areas and many more.

    What kinds of benefits does AIOps provide?

    By leveraging advanced AIOps solutions, IT teams can realize significant benefits:

    • Optimized user experiences. By harnessing predictive insights and fast, automated remediation, IT teams can prevent issues and minimize the impact of those that do arise. As a result, these teams can be much better equipped to deliver optimized digital experiences to end users and customers. 
    • Maximized operational efficiency and staff productivity. Advanced solutions can deliver unified intelligence and automation across today’s modern, dynamic, and hybrid IT environments. With these capabilities, IT teams can eliminate manual efforts, streamline workflows, enhance collaboration, and establish autonomous operations. 
    • Enhanced scalability. With advanced solutions, IT teams can wring maximum utility and value from their staff, infrastructures, and services. Consequently, these solutions make it practical for businesses to scale to accommodate the explosive growth in data, environments, and services. 
    • Increased automation that fuels enhanced reliability. With advanced solutions, teams can enhance infrastructure monitoring with intelligent recommendations and automated remediation capabilities. This can help organizations create more resilient production environments, streamlining their SRE initiatives.

    How does AIOps relate to other IT operations-related disciplines?

    In recent years, a number of strategies, technologies, and approaches have emerged that relate to how IT operations is managed, including DevOps, BizOps, MLOps, and more. This section offers an overview of each of these areas and offers a look at how AIOps relates.

    DevOps

    Traditionally, development, quality assurance (QA), and operations teams worked in a siloed fashion, and weren’t aligned. DevOps is an approach for bringing these teams together, along with related best practices and methods that aim to speed the delivery of high-quality software. 

    DevOps features a number of key principles, including automation and seamless collaboration between all stakeholders within the application delivery and management lifecycle. AIOps promotes both of those goals. In complex, fast-moving environments, the visibility and automation that these platforms provide are essential to effective collaboration between teams. Further, AIOps can help DevOps teams identify potential problems proactively, and better understand the impact of changes and new releases. As a result, teams gain confidence that any new software releases will enhance the user experience, not degrade it.

    BizOps

    Approaches like DevOps are helping to speed the delivery of critical new business services. However, even after successful DevOps adoption, alignment with business objectives remains a missing piece in many organizations. It is for this reason BizOps is emerging as a vital approach. 

    An extension of DevOps and agile methodologies, BizOps is a data-driven approach that aligns IT and business leaders. This methodology helps put business outcomes at the center of everything, from value management to development to IT operations.

    Through their ability to unify visibility of both IT and business intelligence, AIOps solutions can offer essential capabilities to support BizOps initiatives. By tracking performance against business goals and KPIs, teams can prioritize anomalies that are most important. With these capabilities, teams can more squarely focus on optimizing the customer’s digital experience, achieving key business outcomes, and accelerating digital transformation.

    NoOps

    In essence, NoOps can be seen as the ultimate fruition of successful AIOps initiatives. NoOps is based on the proposition that manual IT operations efforts can be eliminated altogether, freeing staff to address other business priorities. 

    Today, AIOps is pushing the IT ecosystem toward a frontier where the realization of true NoOps, and the near total automation of IT operations, is becoming realistic. The best solutions will be ones that can use big data, analytics, and AI to determine how to identify, understand, and react to problems on the basis of what has worked in the past. Going forward, a new generation of platforms will leverage AI to understand unified data sets and make autonomous decisions that take into account the full and unique context of every situation.

    That’s what complete NoOps is all about. Much more than simply automating certain workflows using preconfigured solutions, a total NoOps approach is one where decisions can be tailored to each problem in a unique, customized way.

    MLOps 

    Often, the terms AI and machine learning are used interchangeably. However, there are important distinctions. AI is an overarching concept that can include a range of distinct technologies approaches, which all serve to bring intelligence to an endeavor. Machine learning is a specific means of establishing AI, and is centered on the process of enabling machines to “learn” based on the continued processing of big data.

    Because these terms are often used interchangeably, it is understandable that there be confusion between AIOps and MLOps, which stands for “machine learning operations.” Both of these approaches can contribute to improved intelligence and automation. There are important distinctions, however. 

    MLOps is largely focused on establishing effective algorithmic models and processes. When engaged in MLOps, teams are focused on optimizing the management of these models. This can include efforts to make them more accurate. It can also include trying to improve them from an operational standpoint, such as automating training, monitoring, or deployment.

    AIOps is all about a different end game: boosting IT operations. MLOps can be applied within an AIOps context, but its usage isn’t restricted to this domain. In reality, MLOps can be applied anywhere machine learning algorithms are used. 

    Conclusion

    AIOps has already started fueling digital transformation, optimizing the way IT operations teams work and collaborate with other stakeholders. Going forward, applications will become even more complex and the demand for automation and collaboration will grow more urgent. Consequently, these solutions and methodologies will become inseparable from successful IT strategies. 

    Just as DevOps is a never-ending journey, the AIOps journey is never truly over. Every implementation is unique, but they all involve incremental expansion and improvement upon the ways in which platforms are leveraged. Keeping the progressive nature of AIOps in mind is essential for getting the most out of it and ensuring that you don’t stop before you allow your strategy to realize its full potential.