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Tag: artificial intelligence

  • Eggplant Injects AI Into Application Testing

    Eggplant Injects AI Into Application Testing

    Eggplant has infused its automated testing platform with deep learning algorithms that automatically identify and classify user interface (UI) elements such as buttons and text fields across any platform and then generate all the automation assets needed to instantly run a test.

    In addition, Eggplant has made it easier to visualize the overlap between common application failure patterns that can be used to automatically optimize testing.

    Finally, Eggplant has also revamped its own user interface to make it easier to deploy its platform across multiple test machines.

    Company CTO Gareth Smith said in the months and years ahead it’s apparent AI will be playing a much larger role in application testing. There are simply too many platforms on which application code is now being deployed for testing teams to keep pace manually.

    Each platform requires tweaks to the user interface to optimize the customer experience. At a time when many organizations are accelerating digital business transformation initiatives, the user application experience has never been more critical, he noted.

    Eggplant’s Digital Automation Intelligence (DAI) platform is designed to automate the entire testing lifecycle process as part of a larger best DevOps practice. The rate at which application tests can be built and run can be a bottleneck as applications are being built. The DAI platform looks to first automate the creation of UI tests and then launch them against application code to eliminate as much testing friction as possible, said Smith.

    IT teams also can track user behavior to create journeys based on user interactions with applications and store tests created using modeling tools provides by Eggplant in its cloud service. That approach not only accelerates the application development process, but it also serves to reduce the total cost of application development, which is critical at a time when IT budgets are squeezed because of the economic downturn brought on by the COVID-19 pandemic.

    As part of that effort, Eggplant provides DevOps teams with access to an application programming interface (API) through which they can self-service their own tests. That may not eliminate the need for a separate testing function within the application development process, but Smith noted it does make it easier for developers to discover potential issues on their own versus having to wait on application testers to discover them.

    Regardless of who conducts the testing, the rise of DevOps means there’s more application code to be tested than ever. As such, testing backlogs within many IT organizations are growing. The challenge is finding a way to conduct application testing that doesn’t result in application deadlines being missed. After all, when deadlines are missed, typically the first thing that gets scaled back is testing. Many DevOps teams convince themselves that any issues that come up can be addressed in the next update.

    Of course, once end users reject an application because issues were never discovered during testing, it’s not uncommon for an application to be rolled back. Rather than having to choose between those two evils, it might be better for all concerned to rely more on automation and AI to accelerate the testing process as much as possible.

  • Appian Infuses Low-Code Platform With AI

    Appian Infuses Low-Code Platform With AI

    Appian this week released an update to its low-code application development and deployment platform that employs artificial intelligence (AI) to recommend best next steps, including how to address cybersecurity issues.

    In addition, the latest version of Appian also makes it easier to incorporate robotic process automation (RPA) modules to automate rote tasks in addition to adding support for Google Cloud Document AI, which makes extracting documents for use within an application that employs RPA easier.

    Malcom Ross, vice president of product strategy for Appian, said the machine learning algorithms embedded with the Appian cloud platform make it possible to embrace best DevSecOps practices to accelerate the development of secure applications. With the rise of automated machine learning (autoML) it’s also becoming easier to automate the process of applying machine learning algorithms to application development and deployment, he said.

    While low-code platforms go a long way toward simplifying application-building, Ross said not enough attention is being paid to the deployment process. The goal is not necessarily to roll out more applications faster but to ensure whatever applications are deployed achieve a level of quality that eliminates the need to address cybersecurity issues post-deployment.

    Appian achieves that goal by running automated tests for an application, which now also include Appian Plug-ins to detect potential mismatches in plug-in versions or plug-ins that are missing.

    Ross said that when it comes to AI there always will be some trepidation. However, there are many manual tasks that many developers would just as soon see automated using AI. As AI becomes more widely relied on to drive consumer application experiences, the more acceptance there will be among IT teams to automate internal processes, he noted, adding cybersecurity is one area in which most developers will appreciate whatever help AI can provide.

    AI is not likely to replace the need for a DevOps team or cybersecurity professionals anytime soon. However, at a time when many organizations are massive amounts of economic pressure, it’s clear AI has a significant role to play in reducing the total cost of IT. Despite advances in IT automation, the cost of labor remains one of the most expensive elements of any IT budget. As such, IT organizations have a keen interest in making sure DevOps teams and cybersecurity teams are not wasting time on tasks that might be better handled by a machine.

    Obviously, the way AI systems and IT teams will interact with one another is still being defined. However, the one thing that is certain is platforms such as Appian will be making more recommendations about what needs to be done when. It may take some time for IT professionals to have confidence in those AI recommendations; however, in terms of mistakes that are routinely made it’s also apparent IT teams will soon be running out of excuses.

  • Aruba Injects AIOps Into Network Management

    Aruba Injects AIOps Into Network Management

    Aruba, a Hewlett Packard Enterprise (HPE) company, this week launched a platform that leverages artificial intelligence (AI) to identify and remediate networking issues before they have an impact on end users.

    Patrick LaPorte, senior director of cloud and software solutions marketing, said the Aruba Edge Services Platform (ESP) applies algorithms to data collected for customer networks and aggregated by Aruba in the cloud. That approach enables the company to apply AIOps to automate the management of both software-defined wide area networks (SD-WANs) and Wi-Fi access points.

    Aruba has been making a case for converging the management of SD-WANs and Wi-Fi access points in remote offices for a couple of years now. With the downturn in the economy brought on by the COVID-19 pandemic, the need to reduce the total cost of networking is even more pressing, LaPorte said.

    The Aruba ESP platform further reduces the total cost of networking by augmenting network administrators with AIOps capabilities that will enable a smaller team to centrally manage via the cloud a much larger number of network services being delivered to remote offices, he said.

    At the same time, Aruba enables IT organizations to implement zero-trust network security by providing built-in support for role-based access, dynamic segmentation of network services and identity-based intrusion detection to authenticate, authorize and control every user and device connected to the network, said LaPorte.

    The Aruba approach to AIOps revolves around Aruba Central, a cloud service based on Kubernetes the company relies on to manage networking equipment that runs ArubaOS services. Aruba claims to have more than 65,000 customers using Aruba Central to manage network services.

    Aruba Central has been enhanced to include AI Search, a data discovery service based on a natural language processing (NLP) engine that enables IT teams to employ English-language queries to extract user and device information from the data lake created by Aruba ESP. The company has also added user-centric analytics to better identify client, application and network performance issues.

    Aruba has also added a faster Aruba CX 6200 Switch Series that includes built-in analytics and automation capabilities.

    Finally, Aruba has launched Developer Hub, which provides access to its application programming interfaces (APIs) and documentation to applications that leverage the Aruba ESP platform.

    The way network services are being managed and delivered to remote offices is dramatically changing as applications become more distributed. At the same time, the entire definition of what constitutes a remote office in the age of COVID-19 is being transformed as more employees work from home. Aruba is addressing that challenge by making it easier to set up network tunnels between home offices and remote offices managed by Aruba Central, said LaPorte.

    As networking continues to evolve there is no doubt IT organizations will be relying more on automation and AI to deliver network services as scale. Less clear is the degree to which IT organizations will be willing to standardize on networking equipment from a single vendor to achieve that goal. In theory, IT organizations can reduce their total cost by standardizing on a single vendor. In practice, however, many IT environments are comprised of a dizzying array of networking equipment from multiple vendors.

    Regardless of what path forward is chosen, it’s already apparent the current networking status quo will not hold for much longer.

  • How DevOps Powered by AI and Machine Learning Is Delivering Business Transformation

    How DevOps Powered by AI and Machine Learning Is Delivering Business Transformation

    The use of artificial intelligence (AI) and machine learning (ML) is fundamentally changing the way we think about DevOps. Most notably, it is delivering a new form of DevOps that recognizes the need to have systems that are intelligent by design and underpinned by comprehensive security (DevSecOps). For many, this will be the crucial next step if DevOps is to shorten the software development lifecycle for all connected intelligent systems, ensuring the continuous delivery of secure high-quality software.

    By now, most organizations understand DevOps is a substantial discipline that they must adopt – according to Deloitte, organizations adopting DevOps see an 18%-21% reduction in time to market. By breaking down the silos between business and IT operations, DevOps can ensure consistent levels of productivity, efficiency and service delivery, all of which hold weight in these times of heightened uncertainty.

    To put it simply, DevOps can help businesses compete in already congested marketplaces. Through a foundation of continuous integration (CI) and continuous delivery (CD), organizations can ensure the customer receives the product they demand in the fastest time possible, while mitigating any elongated frustrations experienced from a lack of harmony between systems engineers and operations teams. Incorporating AI and ML into that DevOps strategy will take things to the next level.

    AI and ML Are the Next Evolutionary Step for DevOps

    Today’s companies are data-driven and being built as digital platforms. However, just 28% of organizations are currently succeeding in their digital transformation journeys. Increasingly, corporations are looking at what AI and ML can do to help them realize their transformative ambitions.

    Both the AI and ML markets are expected to experience huge growth over the next three to four years. Analyst firm IDC, predicts worldwide spending on AI systems will reach a href=”https://www.marketresearchfuture.com/reports/machine-learning-market-2494″>42.8% by 2024.

    What AI and ML reveals for these companies, and the increasing value of what Al, ML and devices can do together, are changing the way organizations view the world as they digitally transform. Coupling AI and ML with DevOps will lead to a significant shift in what DevOps gets involved with. 

    Primarily, it sets DevOps front and center of an organization’s wider digital transformation ambitions. If digital companies run on living data, then the development of these intelligent systems is an enticing environment for DevOps to prove its wider value to the organization like never before.

    Security Takes the Center Stage

    According to a TechTarget survey of key IT decision makers, cybersecurity and risk management were found to be the number one area for spending this year, with more than 53% of respondents saying they saw budgets increase in this area. And security is a crucial element that any organization employing a DevOps strategy should be considering. 

    DevSecOps is the simple premise that everyone involved in the software development cycle is responsible for security. In fact, the saying “security is everyone’s responsibility,” is a mantra that has been repeated often, and just as often ignored. Embedding security within every part of the development process, ensures security and compliance monitoring tools keep pace with the speed and agility that DevOps offers. It is a common understanding that security is the biggest block to the rapid and seamless development and deployment of systems, as security solutions have not traditionally been built to test and code at the speed DevOps requires. 

    Ultimately though, more automation from the start, reducing the need for manual configuration from security architect is where DevSecOps shines. This reduces the chance of misadministration and occurring faults, which can lead to downtime, and potential breaches or attacks. Quite often, the best ways to secure your organization is through simple hygiene factors, such as regular patching and software updates. 

    The reality is that every organization employing DevOps should really be approaching it as DevSecOps. Security is of ever growing importance to each and every organization and, through empowering it with AI and ML processes, it can be enhanced too, simplifying the processing of data to easily identify threats or potential vulnerabilities in the security makeup. 

    Reaching Beyond Human Capability

    The future will be made up of connected intelligent systems that span the intelligent edge, all the way to the cloud. The expectations of these systems are based on the lifecycle of a typical mobile application with constant feature enhancements. But, how do you know what to deliver next, especially when things are moving so quickly, and as developers are inundated with information and data from a number of different sources? This data deluge can be powerful, but it can also be overwhelming.

    Crucially, this is where the power of AI and ML comes into play. AI and ML will aid developers in making sense of the information housed across various data warehouses. In DevSecOps, there has always been an approach to automate everything, and AI and ML will be instrumental in automating the analysis and processing of data – a task that is now far beyond the capabilities of humans.

    Incorporating AI and ML will enable developers to better understand and use the data at hand. This will see developers understand not only the error, or the occurrence of a fault, but the detail of what happened in the run up to the fault – vastly reducing the chances of incurring that fault again. AI and ML will also be responsible for enabling the transformation from diagnostics to prognostics across any and all tools and systems, making it easier for developers to anticipate, identify and resolve faults or errors. 

    Increasingly, we’ll see organizations benefit and drive value from real-time insights. They’ll do this through AI and ML frameworks deployed on active systems to deliver optimizations based on real-time development, validation and operational data. This level of real-time actionable insight bolsters DevSecOps’ ability to achieve CI and CD, through improved, reduced mean time to resolution, easing the impact on operations and, ultimately, enhancing the end-user experience. It remains to be seen if incorporating AI and ML into the DevSecOps cycle will achieve the delivery of self-healing systems that can detect and resolve issues without the need for human intervention. With true AI, however, this might well be possible.

    The benefits and potential use cases of AI and ML are widely acclaimed, and while we are still some way from seeing their full potential, the capabilities they currently possess are revolutionizing many sectors. The fact is, if DevSecOps is to continue to keep pace with the vastly growing digital data economy, then it will become reliant on advanced technologies such as AI and ML. Data is powerful if used correctly, but with so much of it to process and analyze, that can be a challenge.

    A move toward secure automated processes is the progressive step that many organizations need to consider if they are to realize their digital transformation ambitions. Organizations cannot continue to do things the traditional way and expect to get the results that the new world is looking for.

  • A Buyer’s Guide to AI and Machine Learning

    A Buyer’s Guide to AI and Machine Learning

    B2B software sales and marketing teams love hearing the term “artificial intelligence” (AI). AI has a smoke and mirrors effect. It sounds impressive. But, when we say “AI is doing this,” our buyers often know so little about AI that they don’t ask the hard questions.

    In industries like the DevTools space, it is crucial that buyers understand both what products do and what their limitations are to ensure that these products meet their needs. If the purpose of AI is to make good decisions for humans, to accept that “AI is doing this” is to accept that we don’t really know how the product works or if it is making good decisions for us.

    When we’re in the buyer role, we often don’t hold ourselves responsible for understanding AI and machine learning (ML) products because these technologies are intimidating. They’re incredibly complex.

    This article addresses the limitations of AI and ML, so software buyers can ask the right questions to understand what they are buying. 

    The Test Oracle Problem

    One limitation of some AI or ML products is that for certain applications of the technology, there is no source of absolute truth to compare against the accuracy of the output. For example, neither humans nor machines know how to produce the perfect set of end-to-end tests for any given application. This is the test oracle problem: there is no objective standard of truth. No one wants to introduce this kind of uncertainty into their sales process. Yet, our buyers deserve well-informed answers about our products.

    As a buyer, you need to understand the intended advantage of your seller’s AI product before making a purchase decision. Is it meant to make a decision that is more accurate—against an objective standard—than a human? Is it meant to make a faster decision with less cost? Or introduce an alternative methodology that uses new data in a new way? Answers to these questions influence how you will use the product and what value it provides.

    AI Versus ML

    Though AI is commonly accepted as “any machine that uses math to make decisions,” true AI is self-taught. AI has a neural net that mimics neurons in a human brain which allows it to teach, update and evolve itself. Because of this, true AI is difficult to build and is often experimental rather than commercial.

    More often, what’s being described when we say AI is actually ML. ML is human-taught: Machines learn through human feedback using a probabilistic decision-making process that improves via ongoing correction. Machines take in data, run algorithms against it and output a decision — or series of assertions — based on probabilities. Humans correct the machine by telling it whether it was accurate in its assessment, and the machine updates. As it receives accuracy feedback, machines learn to make better decisions. And because ML is based on probabilities, it will sometimes make the wrong decisions.

    Based on how you plan to use a product, you need to determine how rigorous its accuracy needs to be. How often a machine can make the wrong decisions and still serve its purpose is application-specific. Self-driving vehicles must be nearly perfectly accurate to be adopted. Paralegal ML toolsets likely need to be less accurate. How accurate does your product need to be?

    Asking the Right Questions

    Regardless of how you plan to use a product, it’s important to ask the right questions to understand the product and build resiliency around its accuracy levels. The next time a seller tells you “AI is doing this,” you can ask the following: 

    1. Is this product an ML product? Does it need to be ML to get a meaningful result? To be ML, a product needs to learn through human feedback, not just make decisions using probabilities. Do you just need a product that uses logic to make decisions, or a product that improves in accuracy over time?
    2. How is the accuracy of this product calculated? You won’t know if the machine is more accurate than humans if you don’t know the conditions used to calculate accuracy. If a machine is 30% more accurate than humans, who assessed this accuracy and how did they determine this?
    3. How do you know when the product makes the wrong decisions? Any ML product will sometimes produce the wrong output. Typically, a seller’s most successful customers have already adopted business processes to build resiliency to this wrong output. If so, the seller can help you adopt them as well.
    4. In its current state, how often does the product make the wrong decisions? Knowing the frequency of mistakes and the stakes of those mistakes will be crucial to deciding how you use the product, and whether it’s safe to do so at this stage in its development.
    5. How many teaching hours have been put into this product? This number will provide a simple approximation of how much effort has gone into making the product more accurate. A low number can be fine, depending on the application.
    6. How does my usage improve the accuracy of this product? As a buyer, you are an integral part of the machine testing and teaching process. You should be willing to use your data to improve their accuracy, because you want these products to improve in the future.

    Why Knowing AI and ML Matters

    Not only is there a lot of “AI” that isn’t AI, but there is also algorithmic technology that isn’t ML. It is thus essential for buyers to know enough to ask the right questions and understand how these products make decisions.

    There are limitations to all ML products, though the limitations differ by product and the way the product is applied. When a product’s accuracy levels are unknown, all you can do is ask if its methodology is valid for decision-making: Does it have access to better data than humans? Can it make smarter and faster decisions than humans with this data? If the answer is yes, you should consider buying the product rather than having your people do the work.

  • OpsRamp Offers Users Free IT Management Services

    OpsRamp Offers Users Free IT Management Services

    OpsRamp announced today that IT organizations that subscribe to its discovery and monitoring tools will now also receive free access to the company’s AIOps and remote access management service tiers for one year.

    Ciaran Byrne, vice president of product for OpsRamp, said the decision to provide free access to additional tiers of service is a direct response to the economic downturn brought on by the COVID-19 pandemic. IT teams are under pressure to reduce the total cost of ownership of IT at a time when they need remote access management capabilities more than ever, he said.

    With so many IT teams now working from home—what may become the new normal—OpsRamp is looking to make it less expensive to transition to a cloud-based platform for managing IT operations, he added.

    One of the byproducts of the pandemic, Byrne said, will be to accelerate the rate at which IT teams are automating processes. Many IT leaders are unsure how many members of the IT team they may be able to retain during a downturn. As such, many of them have already begun to aggressively automate many more processes as part of an effort to enable a potentially smaller IT team to remain effective, he said.

    OpsRampThe challenge, of course, is that the IT environment itself is increasingly complex to manage. In fact, as IT teams look to deploy cloud-native platforms to make applications more flexible and resilient, the IT environment is likely to become even more complex. In fact, according to a recent OpsRamp survey, as of the start of the economic downturn, 69% of respondents expect their organization to invest more in AIOps.

    It’s unclear to what degree the OpsRamp decision to make additional services available for free represents the start of a battle among SaaS IT management platform providers to gain market share. IT teams can expect that, given the current economic climate, these providers will be a lot more willing to negotiate terms and conditions.

    Of course, the biggest inhibitor to adopting a SaaS-based platform to manage IT has been inertia within the IT organization. The rate at which organizations now determine whether to migrate to a SaaS-based platform for managing IT will have a significant impact on the degree to which IT vendors will be willing to negotiate. Of course, incumbent providers of IT management platforms deployed in on-premises IT environments are also likely to be more aggressive in response.

    Not every IT organization will be able to make a fast transition to the cloud during the economic downturn. However, at a time when the pressure to transition to an opex versus capex financial model for IT increases, one of the easiest places to start making that transition is with the software employed to manage IT systems regardless of where they happen to be located.

  • DevOps and AIOps: Better Together

    DevOps and AIOps: Better Together

    DevOps is an attempt to scale technology with humans but AIOps is the ultimate answer.

    In today’s world, where so many essential business tasks have become digitized, IT teams must deal with constant change while ensuring zero downtime.

    The irony is that, although IT has become business-critical, the productivity and agility of the people building and supporting the customer experiences has plummeted. This has serious consequences for businesses. When your apps and services malfunction, you lose customers and revenue drops. When IT teams focus mostly on fixing emergencies, agility suffers and innovation shrinks. You need seamless operations in order to grow, compete and thrive.

    Historically, IT teams grew along with technology, beginning with the mainframe through distributed computing. But, the advent of virtual computing led to a world of microservices and ephemeral logic. Now companies simply generate too much data for humans to monitor and understand manually and with legacy tools.

    This opened the floodgates for an AI-led operations evolution, known as Artificial Intelligence for IT Operations (AIOps). By streamlining and automating IT monitoring, AIOps has helped IT Ops teams regain control over IT environments, quickly detect and repair problems, and prevent outages.

    AIOps

    DevOps: Back to the Future?

    However,  there’s recently been a shift in technology operations to once again attempt to scale technology with humans. The DevOps movement has placed the ownership, support and success of their services on the developers that write the code. This has decentralized technology operations teams that primarily operated with little context and unclear escalation paths, creating several smaller teams of DevOps engineers. 

    DevOps teams work together, each on their own microservice, toward the company’s desired customer experience and business goals. Depending on the operations model, there can also be a dedicated site reliability engineering (SRE) team or individuals on the DevOps team with the primary SRE role of monitoring observability data to discover potential issues. In this scenario, SREs feed the insights they uncover back into the development cycle to correct and strengthen the reliability and scalability of the DevOps teams’ services.

    AIOps

    But, as incidents occur in real-time, the challenge remains with SREs and DevOps teams to achieve insights and awareness across their applications, infrastructure and, ultimately, business services. To understand where incidents are occurring and what impact they’re having on the services and customers, it’s necessary to surface important events from the noise, understand the relationships between alerts and obtain the context needed to engage the right teams and people.

    This leads to another challenge: Getting the right people to respond and resolve incidents before there’s any business impact. Each DevOps team has its own responsibilities and tools. Often, DevOps teams don’t communicate except through their APIs. When incidents occur that need immediate attention from multiple people from multiple geographically-dispersed teams, how do you contact them and get them to collaborate, considering they have complex and different on-call schedules and escalation processes?

    As companies continue to transform with a customer experience focus and a digital-first mentality, they realize the seriousness of these challenges, the magnitude of their monitoring data and its negative effect on their business. Part of transforming their business is transforming their technology operations: Enter AIOps with the light to shine on DevOps processes, leading to a manageable, efficient and profitable future. 

    What AIOps Brings to DevOps Teams

    AIOps provides a unique solution to address operational challenges and cover every aspect of your service assurance strategy and business.

    Bottom line: You need to free people to focus on mission-critical tasks and empower them to build better services for better customer experiences—not scale them along with technology in an attempt to operate it.

    AIOps allows you to scale by integrating with the tools and infrastructure you’ve invested in over the years and by adding critical layers of AIOps (intelligence) throughout. By applying patented AI and machine learning algorithms to your observability and monitoring data, AIOps learns your environment’s normal behaviors and generates alerts accordingly. This must be done at the edge, close to where digital services produce vast amounts of data.

    Based on the anomalies generated both locally and environmentally, AIOps will then surface and correlate important alerts from all sources into actionable, contextual insights. A true, comprehensive AIOps solution also identifies root causes and impacts, and prescribes potential solutions based on previous resolution steps and feedback. This all occurs in a virtual workspace, where team members can collaborate, visualize and provide feedback. 

    AIOps notifies and empowers the right people to take the right action. It simplifies and understands complex team structures, engagement methods, on-call schedules and escalation paths, ensuring the right people are engaged and collaborate, even when they’re located around the globe.

    When a situation or incident is surfaced, it is sent to users in real-time. Based on the insights derived from the underlying data and machine learning applied, AIOps knows the exact teams and people that need to take action, and the ones that need to be informed. These users have the context they need to respond, share a consistent view and stay in sync throughout the lifecycle of the incident. Once the incident is resolved, a streamlined post-mortem speeds up future response, by using similar incidents and predictive analysis to spot repetitive and future problems.

    AIOps: A Key Ingredient for Effective DevOps

    The only way to scale with the technology being created in today’s and tomorrow’s world by DevOps teams is with AI. It frees humans to focus on creating and improving the customer experience that drives maximum profitability for your business.

  • IBM Launches Ambitious AIOps Initiative

    IBM Launches Ambitious AIOps Initiative

    IBM today announced it will apply a range of artificial intelligence (AI) technologies to automate the management of IT operations and modernize applications, also known as AIOps.

    Announced at IBM’s Think Digital conference, Watson AIOps leverages algorithms to continuously optimize IT operations while Accelerator for Application Modernization with AI is a suite of tools that leverage continuous learning and interpretable AI models to help organizations stay current as modern of technology and platforms evolve.

    Made available as part of a Cloud Modernization Service engagement, the Application Modernization with AI tools span a range of tasks from applying AI to more accurately identifying software components that should be deployed as containers or converted into microservices to applying symbolic reasoning to dynamically define modernization steps that are surfaced via a natural language processing (NLP) engine.

    Watson AIOps, meanwhile, applies machine and deep learning algorithms to time series data, semi-structured logs, structured data and unstructured data spanning both IT incidents and human conversations to track the timeline of an issue. It then uses that data to correlate root causes, create an explainable diagnosis and generate a recommendation for the best course of action. Built on top of an instance of the OpenShift platform that IBM gained via its acquisition of Red Hat, the Watson AIOps also leverages semantic search techniques that can relate the current incident to past incidents to provide additional context.

    Rob Thomas, senior vice president for IBM Cloud and Data Platform, said Watson AIOps will be made accessible via Slack and comes integrated with Box cloud services. It also is integrated with IT monitoring solutions from vendors including Mattermost and ServiceNow. IBM is applying multiple AI technologies developed by IBM Research for various Watson services provided by IBM, including a Watson OpenScale initiative to make AI explainable to create Watson AIOps, he said.

    That explainability capability is especially critical when it comes to automating IT operations. In general, the main source of resistance to AIOps is a lack of transparency into how algorithms are determining how any given task should be automated. IT teams are concerned not only with whether they can roll back automation for a specific task but also about becoming locked into a specific AIOps platform.

    It is too early to say to what degree IT organizations will embrace AIOps. There is no doubt that in the wake of the COVID-19 pandemic there will be more pressure than ever to automate, especially among organizations that have had to downsize their IT staffs. However, IT automation manifests itself in many forms, not all of which employ machine and deep learning algorithms. At the same time, AI is being infused into every existing IT management tool, so it’s not clear to what degree a dedicated AIOps platform will be required.

    No one expects AI to eliminate the need for IT staff anytime soon. Algorithms, at best, can only augment IT professionals. The challenge now is defining precisely where that human-to-machine interface should optimally be applied.

  • Moogsoft Expands Reach of AIOps Platform

    Moogsoft Expands Reach of AIOps Platform

    Moogsoft this week updated its AIOps platform to enable IT organizations to construct a virtual network operations center (NOC) that pulls in data from incident management and DevOps platforms for remote IT management.

    Adam Frank, vice president of product and design at Moogsoft, said version 8.0 of Moogsoft Enterprise enables IT teams to aggregate alerts from multiple platforms in a way that enables machine learning algorithms to identify high-priority issues that require immediate attention.

    Moogsoft accomplishes that goal using noise reduction analytics to better prioritize alerts, which are then surfaced via an automated topology visualization tool that makes it easier to identify the root cause of any issue, he said.

    Version 8.0 of Moogsoft Enterprise adds integration with the Amazon Web Services (AWS) Firelens services to ingest EC2 log files, the PagerDuty incident management platform and the Opsgenie incident management platform from Atlassian.

    Moogsoft has also revamped its user interface and enhanced its Workflow Engine (WE) to ease the process of aggregating data from a variety of IT management platforms.

    Even before the start of the COVID-19 pandemic, IT organizations had been moving to embrace machine learning algorithms and other forms of AIOps to help manage increasingly complex IT environments. In the wake of the pandemic, Frank said it’s clear IT teams will be relying more on AIOps platforms. Many organizations simply will not be able to afford to maintain the current size of their IT teams, noted Frank. At the same time, resistance to adopting AI to manage IT is likely to decline because IT staff simply will not have the time to manually perform as many rote tasks as they do today.

    In the meantime, the debate over AIOps continues to heat up. Incumbent providers of IT management platforms contend they already have collected the relevant data, so it makes sense to apply algorithms to data where it already resides. Providers of AIOps platforms such as Moogsoft contend that for AI to be effective at scale machine learning algorithms need to be applied to data that currently resides in multiple IT platforms. As such it makes more sense to apply machine learning algorithms to data that has been aggregated in a single repository.

    Of course, machine learning algorithms will soon be pervasively applied throughout the IT landscape. The decision IT teams will need to make is which platform will ultimately serve as its primary control point for managing enterprise IT.

    While some IT organizations may be tempted to postpone that decision in the wake of the COVID-19 pandemic, Frank notes the pandemic has also highlighted the need to modernize IT management platforms that increasingly need to be accessed remotely. Legacy IT management platforms generally assumed the IT staff would be working on-premises. Moogsoft is designed to enable organizations to employ a virtual NOC that can be accessed more easily from anywhere.

    It remains to be seen just how the management of IT will evolve in the weeks and months ahead. The one thing that is certain is the way IT is managed is never going to be the same again.

  • AIOps 2020: IT Talent Is the Game-Changer

    AIOps 2020: IT Talent Is the Game-Changer

    Artificial intelligence for IT operations (AIOps) is a fairly new catch-all term for any multi-layered development initiative involving big data analytics, machine learning and/or AI to automate and solve business and IT problems. Definitions vary, but most support the notion that AIOps improve IT performance, infrastructure, cloud and operations.

    This is a dramatic shift in how IT operations have been traditionally managed, requiring a digital transformation commitment from the organization and teams involved. Any enterprise, in any industry, with a complex systems infrastructure can benefit from AIOps. However, differences in AIOps strategies can result in benefits realized in unique ways.

    Whether AIOps solutions are homegrown or commercial, they typically fall into two categories: domain centric and domain agnostic. Domain-centric AIOps focus on specific data types and sources, while domain-agnostic AIOps are more open and ingest a variety of data points.

    Kentik is a domain-centric AIOps solution focusing its platform on large volumes of network data, such as NetFlow, SNMP, streaming telemetry and BGP routing data. It can provide companies with AI capabilities to automatically recognize traffic anomalies, predict cost overages, alert on traffic spikes and more.

    Moogsoft, a domain-agnostic example, provides AI for detecting anomalies and alerting ops teams to potential issues, as well as reducing mean time to discovery (MTTD) and mean time to respond (MTTR). It can monitor system events and is a good illustration for how AIOps not only frees IT resources for other important tasks, but does so effectively.

    No matter which option is right for your organization, it is important to start small when testing the AIOps waters. Pick a specific use case, implement it and then expand out as the use case proves itself.

    Any Software or Web Property Can Benefit from AIOps

    AIOps benefits run deep within an IT infrastructure. With the COVID-19 outbreak, the internet and systems running businesses globally are more strained than ever, given work from home mandates, increased traffic and higher digital asset volume.

    This bandwidth stretch hits people personally, too (e.g. Comcast, Netflix, YouTube, etc.). Greater usage results in an expanded attack surface for cyber criminals to target. AIOps consistency, reliability, security and self-sufficiency directly affect a company’s viability and the amount of time required of support teams to address issues that arise.

    Certainly, an organization is in an optimal position when IT teams can use AIOps to proactively address issues before a red flag needs attention. However, when the fires on systems around the world start, AIOps is there to put them out with its ability to quickly inspect and act.

    For example, in mid-March when the COVID-19 outbreak resulted in widespread school/work from home mandates, online services from ABCMouse to InstaCart to Microsoft Teams saw site complication and outages. Any CTO knows that IT operations and AIOps teams surely answered the call to quickly detect and resolve root causes.

    However, tools or knowledge only provide half of the equation necessary to be successful. Many organizations either don’t have access to the right resources to quickly remediate issues, or don’t have the technology necessary to get the job done. For instance, the State of N.J. has an urgent need for COBOL programmers to support IT efforts. Even if N.J. knows the way to solve its problem, valuable time is being wasted trying to find the means for it to be accomplished.

    Top Tech Talent Is AIOps’ Secret Weapon

    Automation is the backbone of technology, but true AIOps panacea is found when the right technologists know how to use the information AIOps provides.

    Think of a medical MRI: Once the technology identifies issues, human intervention steps in to determine how and when to improve and fix things. The same is true with AIOps. It frees an IT team to prioritize the most important items and who should tackle them, instead of combing through endless logs and wasting every developer’s time on break fixes.

    How a highly qualified, on-demand workforce makes a tangible, measurable difference here is a factor not to be underestimated. Talented technologists who can immediately ramp up on teams and systems—because it’s what they do day in and day out—seamlessly augment internal development staff and can address/remediate issues.

    The future of AIOps goes beyond the technology itself and rests in the hands of dynamically built, virtual teams that can address issues just as quickly as the AIOps platforms identify them.