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Tag: AIOps

  • Start Automating Now

    Start Automating Now

    A bit of feedback I received from last week’s post is that I didn’t provide any background, I just jumped right in with examples. So let’s take a step back and talk about the bigger picture.

    It is always a surprise to me when I talk to enterprise friends and associates and hear that automation has not taken over their DevOps processes. Yes, we still (and might always) want the ability to have a person review what is being done and make course corrections if the automation has something wrong, but most of the automation that is available for DevOps is no longer new and should be a no-brainer.

    The ability for a build to block based upon any given criteria and notify Jira or ServiceNow—or whatever issue tracking system you use—while alerting the people responsible for the build is simply obvious … and yet, some organizations aren’t even that far yet. The ability to stop a build, create a ticket, assign it to the appropriate person and give it a priority is built into most of these products. It should be looked at to see if it’s suitable to the organization.

    This goes on and on. We’re now to the point where everything in the toolchain can be automated. IaC and GitOps are tackling the broad operations end, and if the application is 100% public cloud-based, CloudOps simply solves the problem. Automation from check-in to production is possible, with very few places where intervention from humans is needed.

    Given that most of this technology is mature—even though some of it has not been around as long but has broad enough adoption that it can be considered mature (Flux and Argo come to mind here)—it is time for enterprises to start another round of automation. Just like AI won’t eliminate most development jobs this year, automation won’t eliminate jobs, either. But we’ve been so strapped for time for so long that this may be a surprise. Automation might reduce the need to scramble for new team members while asking current employees to give a bit more … over and over.

    So get started. The math that made DevOps appealing in the first place—streamlining to reduce time to market and free up IT hours—is the same math that applies to the current round of automation. The more that can be automated, the more time team members will have to perform more productive work.

    Having mentioned AI in passing, I’ll use that as a segue to include AI in the “automation” discussion. At this time, nearly all of the tools we call AI are simply advanced automation tools and should be considered in the process of increasing IT automation. The ability for an app to run, learn your systems and improve them (be that improvement in development, security or ops) is just advanced automation. In the places where it works well in a given environment, it should be adopted. It is highly unlikely that AI will get worse over time, so even marginally acceptable AI results make it worth implementing, counting on technology advancements and continued training to improve that benefit moving forward.

    So, start automating now. Free your time, take a holiday at the beach, do the same job in less time or, to quote a previous generation, “Work smarter, not harder.” You’re doing the job that keeps the servers humming and users coming, now do it with less effort. And keep rocking it.

  • PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty this week made generally available an artificial intelligence for IT operations (AIOps) platform that leverages the data model embedded in its incident management software to reduce the amount of time required for an AI platform to learn how an IT environment operates.

    Jonathan Rende, senior vice president and general manager for PagerDuty, said that approach enables the PagerDuty AIOps platform to begin surfacing valid recommendations sooner than other AIOps platforms. Other platforms first need to be integrated into IT environments by an expensive team of consultants and integrators, he added. The PagerDuty approach makes the benefits of AIOps immediately available to both IT operations teams and application development teams alike, he said.

    The arrival of PagerDuty AIOps comes on the heels of the launch of PagerDuty Process Automation, a platform for orchestrating automation across multiple platforms that is integrated within PagerDuty Operations Cloud. That capability makes it possible for organizations to centralize the management of islands of automation that have emerged across the enterprise without having to replace any framework that has already been deployed, said Rende. There are also more than 700 integrations between PagerDuty AIOps and the PagerDuty Operations Cloud platform.

    Like other platforms, PagerDuty AIOps relies on machine learning algorithms and an AI model created by PagerDuty to correlate events so that the platform can put them in context. Incident response recommendations are then made, which an IT team can accept and automatically implement or reject. The platform then uses those inputs to surface more accurate recommendations over time, said Rende.

    The platform will also automatically group apply defined rules coupled with its machine learning capabilities to aggregate alerts. That capability reduces the overall alert noise that might otherwise be generated, noted Rende.

    Organizations are looking to invest in AI and automation more heavily at a time when many of them are reducing the overall size of their IT staff, said Rende. The challenge with AI, however, is that it can take six months or more for a rival AIOps platform to learn enough about an IT environment to begin making recommendations. The PagerDuty approach takes advantage of the data within the PagerDuty incident management platform to train an AI model more quickly, in a way that reduces the time to generate valid recommendations to a matter of days, noted Rende.

    PagerDuty has been building that AI model for the last four and a half years with that specific goal in mind, he added.

    It’s not clear how quickly organizations are embracing AIOps, but as IT environments continue to evolve, many once-manual tasks are being automated. There is a natural fear of the impact those advances might have on specific roles within an IT organization, but as IT environments get more complex, there will always be a need for IT personnel that can focus on higher-order tasks, said Rende.

    Overall, the rate of IT staff turnover should also decline as much of the toil that contributes to burnout is also eliminated.

    It may be a while yet before AI becomes pervasively applied to manage IT, but the proverbial genie is not going back into the bottle. The challenge is determining precisely where the line between the man-machine interface with IT environments is today versus where it might be tomorrow.

  • Predicting, Preventing and Resolving Incidents With AIOps

    Predicting, Preventing and Resolving Incidents With AIOps

    IT operations teams, site reliability engineers (SREs) and service providers are on a mission to scale across geographies, expand their digital services and create new experiences for customers. Amid this drive, their backend IT systems are getting more complex. This hinders visibility into applications and makes monitoring and troubleshooting cumbersome. With heightened competition in the market, businesses cannot afford to take hits on revenue or customer experience scores due to issues stemming from their IT stack, such as unknown outages, increased incident management times and data replication.

    Infusing artificial intelligence capabilities into IT operations (AIOps) can raise efficiency through automation, improve security and make a significant impact in warding off downtime. AIOps uses machine learning (ML) to enhance IT operations such as performance monitoring, event correlation and analysis. With AIOps, IT teams, SREs and service providers can predict and prevent outages before they occur and resolve them automatically. Moreover, AIOps uses ML to analyze current and past data to make precise predictions, reducing costs and speeding up return on investment (ROI).

    AIOps Implementation

    Launching AIOps requires a unique approach depending on your organization, its capabilities and needs. This article provides a three-step implementation strategy to help your organization detect incidents before they impact users, automate responses and prevent recurring issues.

    AIOps 201

    Fig: AIOps implementation approach

    With the advanced ability to collect and analyze IT operational data, AIOps is an asset for a range of actions and solutions. When implemented effectively, AIOps can enable IT teams, SRES and service providers to significantly reduce application outage costs by over 60% within a span of 24 months.

    Getting Started With AIOps: Predicting Outages in Applications and Infrastructure

    The AIOps platform can be leveraged by IT teams, SREs and service providers for data gathering, analysis and generation of useful insights. It is designed to enhance operational efficiency, offer predictive alerts, reduce mean-time-to-identify (MTTI) and mean-time-to-repair (MTTR) as well as prevent service outages.

    Recommendations

    • Prioritize onboarding mission-critical applications, such as network monitoring, fault management and service assurance onto the AIOps platform.
    • Determine the best method to ingest data from the different monitoring tools. For example, build APIs, use pre-built connectors, use MuleSoft to automatically collect data from different systems, etc.
    • Choose the right ML model based on the types of raw data, e.g., use an anomaly detection model to identify issues from a dataset or use a time-series model to understand patterns based on historic data.
    • Use closed-loop automation to resolve low-risk issues; for example, automate disk clean-up issues or Java Virtual Machine (JVM) bounce for JVM-related issues.

    AIOps platform

    Fig: AIOps platform

    Assess Your Organization’s Adoption of AIOps to Gain Value

    Organizations contemplating AIOps as a strategy should first evaluate their current maturity and then chart out a path to make the best use of AIOps, which is automated resolution. There are three levels of maturity in AIOps adoption.

    Maturity

    Recommendations

    Level 1: Dashboard

    • Remove operational siloes by integrating the application data sources into unified architecture and ITOM tool.
    • Collect logs from applications and set up alerts that are commissioned to command centers to escalate as per the defined SOPs.

    Level 2: Alarm & Recommend

    • Implement supervised or unsupervised ML algorithms on the unified data to derive insights.
    • De-duplicate and correlate alerts and events through noise reduction to alleviate alert fatigue

    Level 3: Automation

    • Correlate incidents and events with business impacts by leveraging ML algorithms.
    • Trigger autonomous remediation bots spontaneously based on the predictive insights to fix incidents that are likely to happen in operations.

    Identify Applications for the AIOps Platform

    Conduct an AIOps workshop with key stakeholders to narrow down the list of apps and prioritize them for evaluation. Apply specific criteria to prioritize applications that meet the required onboarding standards.

    Recommendations

    • Prioritize onboarding critical apps and outage-causing apps on the AIOps platform.
    • Tag the data e.g., metrics, logs, inventory, topology, to ease the browsing, searching and visualization of data across the distributed analytics repository.
    • Setup an AIOps service and support team, as there is a constant need to maintain and update the platform features
    • Collect all relevant logs, metrics and traces along with data collected from ITOM platforms. A poorly constructed AIOps platform will show incorrect insights and inaccurately reflect the activities in the IT environment.

    Onboard Applications and Leverage AI to Predict and Resolve Issues

    To initiate application onboarding, monitoring tools must be set up to collect data for ML models. Predictions from the models can be integrated into visual dashboards for analysis.

    Automate AIOps

    Fig: Application onboarding

    Recommendations

    • Route both the on-premises and cloud-centric data to the AIOps platform. This will facilitate the prediction of issues in both the environments.
    • Use ML models such as Prophet, Nelson, Arisma, Random Forest, etc., to forecast the time series data, correlate the events and predict thresholds.
    • Choose secure connectors to transfer data in/out of the AIOps platform. For instance, any log flowing into the platform must be sent over transport layer security (e.g., syslog) or HTTPS (API endpoints).

    By implementing AIOps, service providers can successfully reduce the MTTI and MTTR, attaining a 63% reduction in outage costs for applications within 24 months, a 90% reduction in false-positive alerts and a 400+ hours reduction in downtime per annum.

    Ramesh Ram (Assistant Vice President), Sreedhar S K (Associate Director), Sakthivel D (Director), and Rohit Karthikeyan (Manager) also contributed to this article.

  • HPE to Acquire OpsRamp to Gain AIOps Platform

    HPE to Acquire OpsRamp to Gain AIOps Platform

    Hewlett-Packard Enterprise (HPE) this week revealed it is acquiring OpsRamp to gain access to a platform for embedding artificial intelligence in IT operations (AIOps).

    Latha Vishnubhotla, chief platform officer in the office of the CTO for HPE, said the company would embed the machine learning technology developed by OpsRamp across its entire portfolio in addition to leveraging it to automate IT processes the company manages via its GreenLake service for HPE customers.

    Ultimately, HPE will be applying machine learning across the entire IT stack, including applications, to reduce the total cost of IT, she said.

    The acquisition of OpsRamp is expected to close in the third quarter of this year. Terms of the deal were not announced, but it comes at a time when more organizations are getting comfortable with AIOps.

    A recent OpsRamp survey polled 138 IT leaders working for enterprise IT organizations and 127 representing managed service providers (MSPs) that generate at least $25 million in revenue and have more than 500 employees. The survey found the primary reason organizations are embracing AIOps is to improve service and application availability and performance (60%), followed closely by automation of operations (58%) and processes (54%).

    The survey found that the greatest IT operations challenge for enterprises in 2023 was automating as many operations as possible (66%), followed by driving greater efficiency and productivity of IT operations teams and processes (61%). Application-to-infrastructure dependency mapping is the top incident management challenge (64%), the survey found.

    The primary operational benefit cited was reducing open incident tickets (65%), followed by reducing mean-time-to-discovery (MTTD) and mean-time-to-remediation (MTTR), tied at 56%, and automation of tedious tasks (52%).

    In general, organizations are choosing one of two AIOps paths, said Vishnubhotla. Some are waiting to evaluate the recommendations made by the AIOps platform before implementing, while others are looking to completely automate processes so they can focus more of their time and effort on application development and deployment, she said. In much the same way a driver of an autonomous vehicle learns to trust the AI over time, there is a similar transition occurring with AIOps, noted Vishnubhotla.

    The challenge is that it takes an AIOps platform a few months to learn an IT environment. The more dynamic that IT environment is, the longer that process can take. At the very least, however, AIOps platforms have the ability to automate many low-level IT tasks today that conspire to increase the amount of toil DevOps teams would otherwise encounter.

    It’s difficult to determine how pervasive AIOps may be within IT environments, but there is no doubt as organizations look for ways to manage IT at scale, there will be increased reliance on automation. Most organizations can’t afford to continue to hire armies of IT personnel, especially as the global economic outlook remains uncertain.

    In fact, one of the benefits of an AIOps platform is that, unlike an employee, it does not take any days off or suddenly decide to take another job. It also, for better or worse, doesn’t forget anything once it’s been learned. As such, it’s now only a question the degree to which AIOps platforms will be trusted over time to manage increasingly more complicated IT tasks.

  • Large Organizations Are Embracing AIOps

    Large Organizations Are Embracing AIOps

    A survey of 265 IT leaders found the primary reason organizations are applying artificial intelligence to IT operations (AIOps) is to improve service and application availability and performance (60%), followed closely by automation of operations (58%) and processes (54%).

    Conducted on behalf of OpsRamp, a provider of an AIOps platform, the survey polled 138 IT leaders working for enterprise IT organizations and 127 representing managed service providers (MSPs). The organizations surveyed generated at least $25 million in revenue and have more than 500 employees. The survey found monitoring/native instrumentation (55%) ranked as the most important feature of an AIOps platform.

    Bill Talbot, chief marketing officer for OpsRamp, said the survey suggested the initial reason organizations appear to be adopting AIOps is to improve monitoring and overall observability as IT teams continue to find simple tasks—such as discovering what is in their IT environments—challenging to achieve.

    Overall, the survey found the greatest IT operations challenge for enterprises in 2023 was automating as many operations as possible (66%), followed by driving greater efficiency and productivity of IT operations teams and processes (61%). Application-to-infrastructure dependency mapping is the top incident management challenge (64%), the survey found.

    The primary operational benefit cited was reducing open incident tickets (65%), followed by reducing mean-time-to-discovery (MTTD) and mean-time-to-remediation (MTTR) tied at 56% and automation of tedious tasks (52%).

    The survey also found 80% of respondents reported it took less than six months to implement an AIOps platform, with more than two-thirds (68%) noting it takes more than six months to hire engineers with the right skillsets for AIOps. Well over 90% of respondents said AIOps is delivering the level of value they expected.

    Despite advances in AIOps, however, two-thirds of respondents said they are concerned about the accuracy of the data being used to train AIOps platforms. A little more than a third (36%) are also concerned about AIOps deployment causing job loss.

    While there has been a lot of initial skepticism concerning AI applications in IT operations at scale, adoption has now crossed the proverbial chasm, said Talbot. IT teams are finding a wide range of static application environments that lend themselves to being automated using machine learning algorithms, he added.

    Less clear, of course, is the degree to which AIOps can be applied to highly dynamic IT environments where changes to code are frequently made. However, the more routine processes that are automated, the more time DevOps professionals will have to manage more challenging issues.

    At this juncture, it’s not so much a question of whether AIOps will be employed to manage DevOps processes as much as it is to what degree. It remains to be seen how generative AI platforms such as ChatGPT might be applied to IT operations, but it’s apparent that AI will automate a wide range of tasks. It’s not likely those capabilities will eliminate the need for humans any time soon, but it does mean that many of the rote tasks that make working in IT tedious may soon disappear.

  • Before ChatGPT, AIOps Powered Enterprises for Years

    Before ChatGPT, AIOps Powered Enterprises for Years

    Led by OpenAI and its ChatGPT software, a new era of consumer use cases for artificial intelligence is here. These exciting new tools have made AI more accessible to more users than ever before. No longer is this technology the exclusive domain of wealthy corporations.

    However, while the world is now focused on the potential use cases for AI in consumer applications, it’s important to remember that this technology has been part of the business world for years. Behind the scenes, AI has quietly been powering solutions that companies of all sizes have harnessed to improve business outcomes. These technologies have specifically been used by IT teams in the form of artificial intelligence for IT operations (AIOps) to help modernize their companies and operate more efficiently.

    By leveraging AIOps-powered solutions in use cases like observability, overworked and understaffed technology teams are improving their ability to manage complicated, hybrid and multi-cloud IT environments, automate processes and optimize their businesses. As companies prepare for a potential economic downturn and a reduction in resources, these solutions and AIOps are more important than ever.

    Here are four ways that AIOps is helping businesses overcome today’s IT challenges.

    Providing Unified Visibility Into Complicated Networks

    Companies have accelerated their digital transformation efforts in recent years. This has included migrating workflows to public and private clouds, implementing new productivity tools and adopting next-generation technologies. The average business now relies on geographically dispersed workforces and IT environments that use 2.6 clouds and leverage more than 200 apps!

    The result is that organizations lack visibility into these environments. A recent SolarWinds survey found that 54% of technology professional respondents stated they only have visibility into about half or less of their apps and infrastructure. This lack of visibility can lead to problems that negatively impact the return on investment for projects.

    AIOps helps overcome this challenge by providing comprehensive visibility into an organization’s systems, regardless of whether those environments are hosted in on-premises, hybrid or multi-cloud environments. AIOps automatically collects and integrates insights and information across geographies, apps and infrastructures into one dashboard. This provides technology teams with single-pane-of-glass visibility into the state of IT environments.

    Automating Processes & Remediating Issues

    While the visibility provided by AIOps and observability is integral to managing today’s complex IT environments, the technology goes a step further.

    With AIOps, teams can automate processes including the identification and remediation of issues. Given the stakes, this is critical. Companies must ensure their environments are always available and that their performance meets and exceeds customer demands. Even short service outages can lead to frustrated users and a damaged customer experience.

    This is challenging, as an IT anomaly often results in many alerts and a flood of log messages, making the problem difficult for a human to understand and address. AIOps quickly ingests massive amounts of information, provides rapid root cause analysis, data/event correlation and incident management which allows for fast and automated remediation of issues as they arise.

    Additionally, since this technology is always observing and gathering information on potential incidents, it can predict and prevent future issues proactively. By implementing AIOps, companies are taking a step toward autonomous operations or environments that can manage themselves without human involvement.

    Protecting Revenue

    In the face of an uncertain economy, every organization is taking steps to protect its revenue. With AIOps, enterprise technology companies have a powerful tool in this fight.

    By implementing AIOps and observability, companies can quickly resolve and prevent IT issues before they arise. This ensures that IT environments are operating at maximum efficiency.

    With AIOps, organizations can reduce downtime and prevent outages. This ensures that business-critical applications and infrastructure are available and functioning. These capabilities are more important as even short service outages have reportedly been capable of causing millions of dollars in damages.

    Freeing Up Employee Time for Innovation and Efficiency

    Over the last few years, companies have experienced severe staffing challenges, with many organizations finding it difficult to fill critical technology roles. This has led to smaller teams with more responsibilities and less free time. The result has been overworked technology professionals who spend far too much time on manual and menial tasks that do little to support the business’s bottom line.

    By implementing AIOps, organizations can streamline workflows by automating repetitive and routine tasks. Doing so can cut down on the amount of overwhelming metrics technology professionals are required to analyze and, instead, empower those individuals and teams to again focus on innovating and completing projects that support key business objectives.

  • Dynatrace Survey Surfaces State of DevOps in the Enterprise

    Dynatrace Survey Surfaces State of DevOps in the Enterprise

    A global survey of 1,300 CIOs and DevOps managers working for organizations with more than 1,000 employees published today finds more than three-quarters of respondents work for organizations (78%) that deploy software updates into production every 12 hours or less, with more than half (54%) say they do so at least once every two hours. A total of 20% report they are deploying updates every minute or less.

    Conducted by the market research firm Coleman Parkes on behalf of Dynatrace, the survey also finds 90% of respondents reporting that the pace of digital transformation within their organization has accelerated in the past 12 months. More than a quarter (26%) expect the pace of digital transformation to continue to accelerate, the survey found.

    However, 55% of respondents work for organizations that make tradeoffs between quality, security and user experience to meet the need for rapid transformation. A total of 41% of respondents said they must sacrifice code quality and user experience due to meet demands for faster innovation, while more third (34%) also need to sacrifice code security.

    Overall, DevOps teams, on average, spend nearly a third (31%) of their time on manual tasks involving detecting code quality issues and vulnerabilities that conspire to slow the pace of innovation, the survey found.

    To address these issues, organizations plan to increase their spending on automation across development, security and operations by 35% by 2024, as they invest more in continuously testing software quality (54%) and securing production environments (49%), automatic vulnerability detection and blocking (41%) and automating release validation (35%). The average budget allocated to these activities in these large organizations is $9.1 million, the survey finds.

    A full 88% of CIOs said the convergence of observability and security practices will be critical to building a DevSecOps culture, with 94% noting that extending a DevSecOps culture to more teams is key to accelerating digital transformation. Only 27% of respondents claimed to fully adhere to a DevSecOps culture today. The biggest obstacles to achieving that goal are that security teams don’t trust developers (55%), developers perceive security teams to be blockers of innovation (49%) and the silos that exist between teams (36%).

    Organizations expect to increase their annual investment in DevSecOps automation by 2024 an average of 35%, but only a quarter (25%) said applying artificial intelligence to security was a priority. At the same time, only 28% of respondents said they are fully confident applications are fully tested for vulnerabilities before they are deployed.

    A total of 90% said increasing the use of AI for IT operations (AIOps) will be key to scaling up these practices. However, 70% of respondents said they need to improve their trust in the accuracy of AI’s decisions before they can automate more of their continuous integration/continuous delivery (CI/CD) pipeline.

    Bob Wambach, vice president of product marketing for Dynatrace, said there is a clear correlation between that accelerated pace of digital transformation and a need to deliver more frequent software updates that is driving those investments. Most organizations, however, are going to find it difficult to achieve all their goals unless they break down the silos that prevent them from automating and observing modern DevSevOps workflows, he added. Achieving that goal will also require DevOps teams to reevaluate which platforms they are employing as they move to reduce the current level of friction encountered when building and deploying applications, he added.

    It’s too early to say just how soon DevSevOps workflows will define the next era of software development. One way or another, it’s certain that every organization that embraces DevOps will need to address lingering application security issues.

  • Complexity is Still With Us

    Complexity is Still With Us

    We have come a long way and had a lot of improvements over the years, but we still struggle with the ability to simplify things faster than we create complexity. It isn’t enough that each new wave of Really Important Bits™ comes with its own issues and layers and complexity; we also haven’t been cleaning up our previous Really Important Bits™ projects. We haven’t cleaned up the successful ones and we haven’t cleaned up the failures.

    It is time to start. Honestly, we are looking at an extended stretch of ‘Do More With Less,’ and we need to get our houses in order. Want to do more with less? Start by standardizing. Yes, that one project that used Really Important Bits™ (or its companion product Really Cool Tech™) was neat and all, but keeping a technology and its layers and pieces laying around for a single project is not great management. In fact, it isn’t management at all; it is letting things happen. I’d call it chaos, but then we’d have to have a whole conversation about Dungeons & Dragons and, frankly, it is happening in the forum of the overall enterprise architecture so it’s not actually chaos, it is simply excess technical debt.

    DevOps was never meant to be a destination, but on this cross-country road trip, our IT car will get filled with garbage if we don’t take the time to clear out the Really Important Things™ of last year or the last management team or the latest merger. We cannot continue to layer more on without relieving pressure somewhere. AI has the potential to offer some pressure relief in some areas, but we were already behind, so for a variety of reasons, it isn’t the silver bullet we need. Those reasons are simple–it is less secure to leave 50 different ways to do the same thing laying around. AI is resolving some problems and freeing up time, but you’re still wasting time and money on redundant or unused Really Important Things™.

    Some things simply cannot be cleaned up without a massive investment. That’s fine—I hope after years of using DevOps methodologies you have them on a list of things that you’d like to upgrade/replace, but it just isn’t feasible to do so. Focus on the things that you can simplify. The famous low-hanging fruits are fine, just like simplifying things in the early days of DevOps meant that each piece of low-hanging fruit freed up more time to focus on other issues, the same is true now that the low-hanging fruit is generated from DevOps. Or DevOops, in some cases.

    How did I get here, you ask? I was writing a simple overview of Git, CI/CD, DevOps, DevSecOps, GitOps, etc., etc. for a newer technologist, and realized that in between those important parts are a whole lot of tools and technologies we thought were important, but which are no longer terribly useful or necessary. I did her the favor of not including the by-the-wayside technology spaces and product markets. I removed the complexity, you could say.

    And keep rocking it. No matter how far along you are in your IT career, you can look back at all of the things you have learned. Apply that learning to simplifying and improving. Reduce overhead by reducing redundancy and look forward to the next Really Important Thing™ you’ll get to learn about. Keep improving—both your knowledge and the environment. And we’ll keep cheering you on.

  • Scaling Predictive Analytics With AIOps to Drive Next-Gen SRE

    Scaling Predictive Analytics With AIOps to Drive Next-Gen SRE

    Enterprise systems are only as valuable as they are reliable, in the sense that they don’t suffer excessive breakdowns. Otherwise, companies experience costly downtime and added stress for engineers due to the additional burden of managing issues. This critical function of ensuring systems run reliably and optimally, at production scale and with minimum human intervention, is the purview of site reliability engineering (SRE) teams.  

    The SRE professional’s job is to reduce human toil by developing and implementing reliable and highly scalable systems that optimize software and applications. Their goal is to proactively identify and, where possible, anticipate potential disruptions to minimize risk and ensure optimal system performance. However, to be successful, SRE teams require a level of enterprise-wide visibility that can be difficult to provide without the right tools to capture and analyze data at the appropriate level of detail. 

    Let’s examine how artificial intelligence for IT operations (AIOps) can support the SRE mission by connecting data enterprise-wide for unprecedented visibility and control of systems and processes, enabling auto resolution of most issues and more efficient triaging for the few remaining cases where SRE teams must collaborate around a fix.

    Challenges of Complexity and Scale in Pursuing SRE Excellence

    As more enterprises digitize their operations and move to greater automation, their IT operations must leverage all data assets skillfully in order to improve reliability and reduce human toil. The SRE profession arose to satisfy this need, with a focus on monitoring systems, accessorizing automated releases, understanding change impacts and automating some of the most common system processes. 

    To do their job effectively, SRE teams need a wide variety of data and analytics capabilities at their disposal. This includes the ability to deep dive into descriptive and diagnostic analytics to look backward and at present conditions to discover what happened in the past and why and to baseline current operations. But this is just the beginning. The true value  of SRE comes with the scaling of predictive and prescriptive analytics to draw on that historic data and apply detailed analysis to generate predictive insights into what is most likely to happen in the future; these insights are the basis for identifying proactive measures that can serve to address any potential problems and optimize those future outcomes, minimizing adverse impacts on operations 

    These analytic capabilities must be fed by robust data that comes from across the entire IT estate. Developers assigned to the SRE role face an especially strong mandate to have this holistic visibility as they plan, build, test, release, monitor and secure systems; their job is hampered to the extent that organizational silos or problems of scale get in the way of achieving that visibility. It is here that AIOps can help evolve IT operations to become more proactive and autonomous by scaling the power and reach of predictive and prescriptive analytics. 

    AIOps Empowers the SRE Mission Enterprise-Wide

    AIOps is an essential tool for the SRE community in the battle to reduce operator stress, configure IT systems to be more stable and run efficiently with less human intervention. AIOps employs artificial intelligence (AI) and machine learning (ML) for observability, context, normal behavior analysis and automated health diagnostics. This, in turn, enables anomaly detection in real-time and closed-loop automatic resolution of most issues. 

    For example, AIOps can uncover patterns showing that, 90% of the time, a particular alert in the organization’s payment system triggers a seemingly unrelated alert within 15 minutes. Moving forward, this gives SRE teams a 15-minute head start on addressing that secondary alert; the discovery forms the basis of an auto-resolution or triaging scenario that uncovers the underlying cause of both alerts and provides a permanent fix. 

    That’s just one illustration of how AIOps uses advanced analytics to discern subtle patterns in data to predict when and where problems may occur, so proactive fixes can automatically be prescribed to head off those problems. In this way, AIOps is the technology backbone that scales and automates the SRE team’s insight and control across all system assets and dependencies, a critical tool for continuous optimization of performance with minimal human intervention. 

    Conclusion: The Game-Changing Role for AIOps in SRE

    AIOps is a game-changer for the critical function of site reliability engineering. Powered by a potent blend of advanced analytics on robust data coming from systems across the enterprise, AIOps delivers unprecedented visibility and control for SRE teams in their mission to reduce toil and ensure the reliability and resiliency of enterprise systems. The result is less human intervention and enhanced value from IT systems that are made more robust, more stable and more efficient.

  • Stable Diffusion Goes Public — and the Internet Freaks Out

    Stable Diffusion Goes Public — and the Internet Freaks Out

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters. (more…)