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  • 5 Reasons Why DevOps Needs AI

    5 Reasons Why DevOps Needs AI

    AI and DevOps together make a powerful combination of efficiency and intelligence

    Supervising and managing a DevOps environment can be complex. The proliferation of data has made it challenging for DevOps teams to effectively absorb and implement information to evaluate and tackle customer issues. Imagine a team navigating through data in exabytes to search for important events that triggered an event; they would end up investing hundreds of hours in identifying the issue.

    A lot of such critical issues can be resolved with artificial intelligence (AI)-powered technologies. Organizations can transform their DevOps environment by deploying AI systems. Since it is difficult for humans to handle large volumes of data, AI has gained traction as the solution that transforms the way teams develop, deploy, deliver and manage applications. Before understanding the role of AI in transforming DevOps, however, we first must understand how AI and DevOps are interrelated.

    DevOps and AI Work Together

    DevOps takes a business-driven approach to deliver, and AI can be integrated into the system for improved functionality. By using AI-powered technologies, DevOps teams can utilize volumes of information to the best of their ability, which in turn helps streamline testing, coding, releasing and monitoring products with precision and efficiency. It also plays a central role in improving automation, quickly identifying and resolving issues and enhancing collaboration amidst teams.

    AI provides a major boost to DevOps efficiency. Various AI tools enable users to map and integrate data at the speed of business, driving business decision-making and delivering delightful customer experiences. Machine learning systems can simplify the way DevOps systems work with data to deliver value.

    Here are five reasons why implementing artificial intelligence-based solutions in a DevOps culture is necessary:

    1. Increased Data Access

    The lack of unfettered data access is one of the most important issues that surfaced in the DevOps environment. It inhibits business users’ capability to leverage data for making decisions, ultimately making organizations difficult to do business with. AI-powered data mapping technologies enable them to deal with data silos that can help them get the right information at the right time so they can make good decisions. AI-powered data mapping, for example, helps users map troves of fragmented data faster, which can then be integrated into a unified database. They can employ machine learning algorithms to map data faster without succumbing to inaccuracies or errors.

    In short, machine learning data mapping tools empower users to map big data with ease and precision. Moreover, they can integrate the data silos present across the business ecosystems to gain accurate customer insights to improve data quality and efficiency. So, AI can tackle organizational silos to help users make decisions without compromising speed or accuracy. AI can combine data from multiple sources and get it ready for consistent and repeatable analysis.

    1. Timely Notifications and Alerts 

    DevOps teams must have good alerts and notifications systems to identify flaws without delay. There are times when notifications come in enormous numbers, all marked with the same severity. Such huge numbers can have a negative impact on the response rate of teams. AI can help prioritize the responses based on the information derived from past behavior, the intensity of the alert and the source of the alerts. As a result, teams can manage the tsunami of data with confidence.

    1. Improved Execution Efficiency 

    The transition from a rule-based, human management of analysis to self-governed systems can be realized through AI. This helps business users alleviate the complexity of analysis and enable a level of change adaptation for digital transformation.

    1. Anomaly Detection Capabilities

    For any business, protecting security systems is extremely important. AI and machine learning can help enhance security by enabling an encrypted data environment wherein, only authorized personnel have the authentication to access data (AI-powered data integration tools play a vital role). Machine learning algorithms help users streamline anomaly detection to boot. Simply put, combining AI and DevOps will facilitate performance and stop breaches or thefts.

    1. Efficient Collaboration

    The minimum the disruption is, the maximum is productivity and collaboration. And AI mechanisms help developers release code at high velocity to minimize the disruption. AI can create a collaborative DevOps environment that helps companies deliver maximum value, maximizing ROI and ultimately making them easier to do business with.

    AI-powered systems can support the teams by offering a single, unified view into data and applications across the chain of DevOps. Along with that, it can improve the accuracy of data analysis as anomalies can be detected and rectified right away.

  • The Weakest Link in DevOps for Financial Services

    The Weakest Link in DevOps for Financial Services

    Many companies in the financial services sector are making huge investments in IT transformation these days. They are embracing DevOps and agile best practices, modernizing legacy applications and moving their infrastructure to the cloud.

    In many instances, though, this significant investment of time and resources simply isn’t producing measurable outcomes that matter to the broader financial services organization. Developers may be coding faster and new operating efficiencies may be realized, but little else has changed. Teams are simply unable to move the needle in support of core business strategies that determine success or failure in the marketplace.

    Why the disconnect? Often those assigned to IT transformation projects work in isolation. They have no way of knowing whether they are delivering what customers need, when and how they need it. What’s more, they are unable to deliver the metrics and feedback needed by product development and strategy teams to guide continuous improvement and innovation.

    Is it any surprise that during tough times, transformation teams are often among the first on the chopping block in the financial services space? They have simply been unable to demonstrate that the significant investments made in their work are worth it.

    A 2-Step Solution

    If your DevOps team is committed to modernization, consider this two-step approach to resolving the disconnect between the work you do and the core business you serve.

    First, don’t work with blinders on. Broaden your mindset and involve all critical stakeholders, from business strategists and developers to operations specialists and customer-facing support teams. Engaging this broader group of constituents can help you gather multiple perspectives on what success looks like and where you need to focus your time and attention. You can determine what outcomes truly matter to your organization and what criteria you need to monitor to demonstrate the value you produce.

    Second, it’s time to rethink testing and the role it plays in your organization. Adopt an automated, technology-agnostic platform that can provide continuous feedback to everyone on your expanded team, not just developers writing code.

    The experiences of a global 500 banking and finance company show why a broadened approach to testing matters. The company was treating testing as an event, part of a linear application development process. Projects were prioritized, code was written and testing was conducted to determine whether the work was bug-free.

    Test creation and maintenance relied on manual effort, making it impossible to keep up with the continuous code development of agile teams. In addition, proprietary testing tools were difficult to use. It was impossible to shift left for continuous integration of fully tested code early in development. And it was impossible to shift right to monitor and learn from application performance and customer experiences.

    As a result, the company lacked a continuous loop of predictive insights and feedback that could be accessed and used by stakeholders across multiple geographic locations to advance the priorities of the business.

    The bank rethought its approach and adopted a new artificial intelligence-powered platform based on open source standards. Now all stakeholders can run tests and monitor services from anywhere, with tools that are easy for anyone to use. And they can do so without sensitive data ever leaving their network. They have the insights they need to create focus and deliver results that matter.

    Making a Smart Investment

    If you’re ready to broaden your DevOps mindset and make the move to continuous testing, keep the following tips in mind. Look for an open source, enterprise-ready test automation environment that is easy to use and integrates seamlessly with your existing testing tools. In addition, insist on the following features that will help you broaden the strategic impact of your testing and monitoring program:

    • Single-pane business insights: Select a solution that helps you keep everyone on the same page, enabling a continuous flow of predictive insights and feedback that is easily accessed and understood. Make certain you can track trends from multiple builds and can share interactive test results and graphs with all your teammates and stakeholders.
    • On–demand scalability: Many legacy tools have limited load-generation capacity. Look for a solution that can help you overcome that hurdle. You should be able to upload tests and emulate network conditions on demand across multiple geographies, while simulating millions of virtual users.
    • Decentralization: Your new test environment should let you broaden your testing program by shifting both left and right as you orchestrate new releases. Make certain you can easily broker new automated tools for continuous monitoring and empower all stakeholders to use them. This democratic approach will help you overcome manual testing bottlenecks and promote faster intervention when issues arise.
    • Self-service test creation: Make certain the user interface for your new platform is “load and go,” with no proprietary languages or special skills required. Anyone should be able to create small-scale, self-service tests from a local machine, then seamlessly reuse the same tests at scale via the cloud to accelerate test automation as part of your continuous delivery pipeline.

    The Payoff for Financial Services Firms

    It’s time to make certain your transformation efforts are headed in the right direction and that your team isn’t working in isolation from the broader business. Expand your mindset and your toolset to encompass key stakeholders and to align with your organization’s business strategy.

    Embed test automation into every activity across your software delivery life cycle and create a continuous feedback loop that informs planning, development and operations. When you do, you can turbocharge velocity, improve quality, boost efficiency and keep pace with customer expectations, all while supporting what matters most to the success of your enterprise.

  • AI Automation Won’t Replace Jobs – It Will Replace Processes

    AI Automation Won’t Replace Jobs – It Will Replace Processes

    As Software 2.0 takes shape, how are AI processes—and the jobs that go with them—changing?

    When it comes to new AI automation, the Software 2.0 movement can be seen as akin to the auto industry’s recent evolution. Beginning in the 1990s and rapidly progressing into the 2000s, the auto industry saw a huge rise in AI automation such as digital vehicle diagnostics.

    Throughout this, people worried that digital transformation might take jobs away, when instead it led to “greater profits, productivity, and competitiveness,” according to a 2008 study by the journal of Technological Forecasting and Social Change. In short, these innovations didn’t replace jobs, per se; they simply changed the processes of jobs from the manufacturer’s assembly line down to the mechanic’s garage.

    An auto diagnostic specialist used to perform tedious tasks such as counting flashes and converting them to error codes on printed-out tables. Computerized diagnostic tools made this process faster, more reliable and, you might imagine, far less tedious. According to diagnostic specialists Helmut Frank and Uwe Schmidts, digital transformation went from being “a necessary evil to being a key to new, interesting and innovative functions.”

    You might liken the above example to the innovations that AI automation and machine learning (ML) have offered to software developers, who until recently, were tasked with a much more hands-on approach to deploying and maintaining applications. In many ways, DevOps practices have heroically come in to break them free of this minutiae, automating many of the repetitive tasks involved in application management. By design, it’s increasing efficiency and security while decreasing tedium and the chance for human error.

    Speaking to tedium, Mike Loukides and Ben Lorica, in their article “The road to Software 2.0” noted, “Up until now, we’ve built systems by carefully and painstakingly telling systems exactly what to do, instruction by instruction. The process is slow, tedious, and error-prone; most of us have spent days staring at a program that should work, but doesn’t.”

    In short: Much like digital transformation brought efficiency to the life cycle of automobiles, AI automation is offering efficiency to every stage of the software development cycle.

    Don’t Change the Players, Change the Game

    Indeed, a new generation of AI automation and ML tools is now emerging, shaping what some call a Software 2.0 movement. But before you begin to fear that these tools will lead to a “they took our jobs!” revolt, consider that they may be celebrated instead as a means of not only augmenting the work of software developers but also helping offset the ongoing developer drought. According to a 2019 Stack Overflow survey, over a third of developers named “not enough people for the workload” as their primary challenge to productivity. Rather than replacing jobs, automation should be viewed as offering efficiency in an industry that desperately needs it.

    As an example, the process of writing code, though more streamlined today than it was 25 years ago, has remained a mostly manual process. This has presented an excellent opportunity for the automated processes we are beginning to see, which bring in AI to automate aspects of code development such as program synthesis, smart code completion and static code analysis.

    Assaf Araki spoke to this in the article, “Software 2.0 takes shape.” Araki described how automation is filling gaps in many other areas, such as regression testing, code analysis, AI for vulnerability detection and observability tools to monitor software health.

    From Monolith to Microservices

    Just like the recent, mysterious disappearing monolith in the Utah desert, the relevancy of monolithic development may be on its way out.

    In the past, software cycles were longer. Teams updated monolithic-style deployments only monthly or yearly. Nowadays, companies are under pressure to push much more rapidly, often turning to microservices and progressive release strategies. This new paradigm requires much automation to increase release efficiencies and enable a seamless CI/CD.

    As Araki pointed out, “Automation in software is best viewed through the lens of Continuous Integration (CI) and Continuous Deployment (CD).”

    How to Know When AI Automation is Worth Its Salt

    It’s smart to consider the investment of both money and time when deciding whether an AI solution is worth adopting. “AI systems need to add value that exceed any new costs and requirements (such as the need for labeled data, hiring new staff, etc.),” Araki said.

    You should also be asking if the solutions are wide-ranging enough to meet the needs of your developers and anyone else on the team who may be employing them. Key considerations for AI automation, Araki noted, are focusing on tasks and systems that are frequently used, adjusted or tuned.

    4 Examples of Versatile AI Automation and ML:

    1. Data management systems from simple systems that employ ML to more complex systems that enable self-driving data management.
    2. Dashboards that are automatically created, ideally with intuitive and visually appealing UX, that use AI automation and ML to bring up the right elements—more progress is expected in these areas in the coming years.
    3. Websites and mobile apps that use AI automation to become highly personalized interfaces, such as those that employ real-time data for online and reinforcement learning—these are also expected to progress in the coming years.
    4. UX and design tools that use simple automation to design and revise digital media, with ML being a promising way to kick the automation up a notch, such as deep learning for game developers.

    AI Automation and ML to Grant Opportunities

    In short, Software 2.0 doesn’t currently appear poised to threaten jobs or decrease developer need. Instead, it seems to be changing the processes engineers are using and creating efficiencies in their work, at a time when their expertise is in increasing demand. Demand for novel roles, like the Cloud Economist, demonstrates the emergence of new opportunities for humans with cloud knowledge who work alongside AI.

    The new evolution of AI and ML also could create a unique subset of developers with skills keeping par with these new innovations—much like digital transformation in the auto industry led to evolving specialties in computerized vehicle systems.

    Not to mention that just as low-code has paved the way for citizen developers and AutoML led to citizen data scientists to fill large gaps in demand, AI and ML solutions have the potential to further broaden and democratize software and code development.

  • Dynatrace Adds Security Module to Observability Platform

    Dynatrace Adds Security Module to Observability Platform

    Dynatrace announced today it is adding a module to provide continuous runtime application self-protection (RASP) capabilities to its observability platform.

    The Dynatrace Application Security Module leverages existing Dynatrace tracing and artificial intelligence (AI) technologies to detect threats in real-time and then apply appropriate policies to automatically secure applications.

    Andreas Lehofer, chief product officer for Dynatrace, said that with the rise of highly dynamic microservices-based applications, the time has arrived to extend the company’s platform into the realm of application security. In contrast, there aren’t typically as many changes to monolithic applications to justify adding a security module to an application performance management (APM) platform, he said.

    Lehofer said the Dynatrace Application Security Module has also been designed to reduce false positives by only surfacing alerts concerning code that is actually running. Rival RASP platforms will detect modules and start generating alerts about libraries that may be present in an application environment but are not actually employed, said Lehofer.

    The Dynatrace Application Security Module working with the Smartscape topology mapping and PurePath tools for analyzing code also make it possible for DevSecOps teams to better understand dependencies between microservices, he added.

    At the core of the Dynatrace approach to DevSecOps is its OneAgent software for application instrumentation, which collects data and feeds it back to an observability platform that includes an artificial intelligence (AI) engine dubbed Davis. As the Dynatrace Application Security Module continues to evolve, IT organizations should expect Dynatrace to apply AI capabilities to application security more broadly, Lehofer said.

    The ability of DevSecOps teams to automatically remediate applications that are discovered to have significant security issues depends on their ability to roll back applications after they have been deployed. Most IT organizations at the moment still have to manually roll back an application after it’s been deployed, he noted.

    Longer-term it’s not clear to what degree AI might advance DevSecOps; however, the more automated the process of identifying vulnerabilities becomes, the easier it should be to automate remediation.

    In the meantime, as the observability capabilities of platforms such as Dynatrace extend into application security, the easier it should become for organizations to embrace best DevSecOps practices by employing a module that is added on to an existing APM platform.

    Dynatrace, of course, is betting microservices-based applications will increase the adoption of its observability platform. Historically, IT teams tended to employ an APM only to instrument their most mission-critical applications because of the costs involved. However, as IT environments become more complex in the age of microservices, platforms that can observe all the dependencies that exist in a distributed computing environment are now critical. Extending that platform to include application security further extends the economic case for investing in an observability platform.

    It may be a while before most organizations reach a level of DevSecOps maturity that requires continuous observability, but as responsibility for application security continues to shift further left, it’s now only a matter of time before more automation is applied.

  • Building Your Own AIOps Platform Is a Bad Idea

    Building Your Own AIOps Platform Is a Bad Idea

    It takes considerable expertise to not only build, integrate and maintain an AIOps platform

    In the wake of the economic downturn brought on by the COVID-19 pandemic, investments in digital business transformation have accelerated. The applications that drive those processes are not only highly distributed, but they also operate at a level of scale that no IT team can manage using legacy approaches to managing IT. It can take weeks to discover the root cause of any issue.

    Enter AIOps. Machine learning algorithms make it possible to reduce the time it takes to resolve any issue and enable IT teams to continuously optimize IT environments at any scale. Many aspects of AIOps, however, are still largely unexplored. Rather than opting for proven platforms, some IT teams are building custom solutions in-house.

    I have experienced firsthand several large enterprises that have embarked on this risky journey, including one Fortune 500 company that asked a partner to help develop a solution and ultimately deployed a commercial product. Enterprise IT teams go down this road to solve a specific tactical problem, such as alert noise reduction. In many cases, all the time and effort put into a do-it-yourself (DIY) project simply winds up being wasted.

    Why DIY Platforms Usually Fail

    Given the prevalence of open source AI tools and frameworks such as TensorFlow, Theano or the Microsoft Cognitive Toolkit (CNTK), it can be tempting to build your own custom AIOps platform. It takes considerable expertise, however, to not only build an AIOps platform but also integrate it into an enterprise and maintain it.

    Here are the leading reasons why in-house developed AI projects are risky:

    • You’ll need a properly constructed data lake: AIOps platforms require access to data residing in multiple technology silos in real-time. IT teams that build their own AIOps platforms need to make sure they are gathering all the right log data, metrics and traces alongside data collected from IT service and incident management platforms. These comprehensive data sets are needed to train whatever machine learning framework is in place, which is often selected at random. Invariably, that means building or buying a costly big data platform to create a data lake to store all that data. A poorly constructed AIOps platform will be worse than the proverbial disease it’s meant to cure because the insights don’t accurately reflect what’s actually occurring in the IT environment. Do you have the funds for this and experienced data science experts onboard to get this right?
    • Designing AI-enhanced workflows is unlike other workflows: Getting the data is just the beginning. Determining how the system behaves and affects existing workflows is the next step. IT teams must decide to what degree they merely want the AIOps platform to passively surface recommendations based on what’s observed versus automatically resolving issues based on defined parameters.
    • Deployment is complex: After developing a few AIOps algorithms to produce meaningful results, the next step is to determine how to deploy it in a resilient and performant architecture. What other systems does it need to integrate with and how will results be monitored and viewed?
    • Monitoring user impact is critical: How will end users interact with the algorithm and what is the ideal UI/UX and workflow? How will feedback be provided by end users for improvement and adoption success?
    • AIOps support and maintenance is not a project, but a team. Ultimately, an internal IT team would need to build the equivalent of a product which needs ongoing maintenance and support. The total cost of the custom platform starts to rise as the bulk of the IT team could wind up spending most of their time managing the AIOps platform instead of making continual improvements. Even if the IT team has the expertise required to build an AIOps platform, there’s no guarantee those individuals will always be available to maintain and update it. Very few IT professionals spend their entire career at one organization.
    • Keeping pace with marketplace innovation: Finally, AIOps as a field is still relatively nascent and the startup community has hundreds of millions of dollars in VC backing to support R&D. Advances are being made at a rate most internal IT teams can’t keep up with, let alone evaluate and vet on their own.

    How to Safely Encourage AIOps Exploration

    There’s no substitute for knowing where an organization needs to go and how to get there. A commercial AIOps platform incorporates all the best practices that have been defined by legions of IT experts, along with these benefits:

    • Faster time to value: You can embark on the AIOps journey much sooner. A commercial AIOps platform will begin surfacing insights in a matter of weeks. It will take an internal IT team months to build an equivalent platform with no guarantee of success. Time is better spent on user adoption and adding and refining use cases for business benefit.
    • Seasoned experts: A commercial platform provides immediate access to not only a proven framework but also, AIOps experts who can troubleshoot and optimize issues quickly. There’s almost no AIOps challenge they haven’t seen before.

    How to Measure ROI From AIOps

    Savvy organizations that invest in AIOps are primarily betting on a better way to manage IT that will enable them to accomplish more as a business. The real value proposition of any AIOps platform is that it enables an existing IT team to do more not by just eliminating rote tasks but also making it possible to deploy more applications reliably without adding IT staff. It’s worth remembering that the cost of labor continues to be the single biggest IT expense.

    The return on investment from an AIOps platform can be easily calculated by measuring:

    • The number of incidents resolved in a given period of time.
    • The size of the IT operations/incident management staff before and after an AIOps platform is deployed.

    Arguably, however, those savings pale in comparison to the opportunity costs of using advanced technologies which can increase operational intelligence and deliver new business value in the form of excellent user experiences and high-performing digital services. IT organizations that don’t embrace AIOps will be unable to compete with faster, more nimble rivals that have modernized their IT processes. The easier, more affordable and least risky way to go about this is by deploying a solution already proven and in market.

  • Want To Deliver Better Software Faster? Use Automation

    Want To Deliver Better Software Faster? Use Automation

    Despite the evident evolution of the software industry, many enterprises continue deploying testing practices that require intensive time and effort, while also facing increased pressure to do more and work faster. I like to call this phenomenon “scattered islands,” as it describes the disconnect in maturity and capability across the testing landscape. The key differentiator here is automation, the impact of which can be found throughout all the phases of software testing, from test planning to defect analysis repair and feedback loop. Even so, the industry has just begun unlocking the true potential of automation in testing.

    AI-driven automation holds the potential to completely transform software testing from an end-to-end testing lifecycle perspective.

    Gap Between Knowledge and Action

    The shortage of test automation is one of the biggest barriers when it comes to releasing new software to the market. In fact, most software testing is still done manually. Enterprises are embracing Agile and DevOps approaches for faster software release cycles to meet customer expectations. However, the bulk of the testing life cycle—especially the planning and design stages—continue to be manual and inefficient. Undetected software quality issues translate into production. The reduced speed-to-market is costing businesses trillions of dollars.

    Strangely, the software industry has yet to embrace automation applications. According to a  study by Kobiton, more than half (55%) of respondents believe automating testing would be an asset to software quality. And yet, most aren’t doing it. In that same study, 76% of respondents say they manually conduct most of their testing; 73% are running at least 100 tests prior to each software release manually—not automatically.

    How Software Testing Is Adopting Automation

    Organizations are starting to deeply consider how to adopt assisted and automated practices across the testing life cycle. That spans from test creation to data management and beyond. Automation is quickly becoming critical for effectively meeting customer requirements, because it offers important benefits: faster time-to-market, increased efficiency and improved quality.

    Now, most organizations are applying automation little by little to improve productivity and stay at par with the demands of development and release cycles. For instance, they may apply automation to test execution or defect management. However, this stilted approach is inadequate for today’s scale.

    Some have speculated that organizations may be leery of full-scale automation due to the looming fear that it would cost the testers their jobs. This is a misconception. Automation isn’t taking jobs, but it is changing them. Testing will become more about predictive rather than preventive methods. With the agile development environment, these methods are being implemented starting with the planning phase.

    By conducting testing through automation tools, organizations can address issues faster. And that means testers will be able to upgrade their skills. With their deep domain expertise, they’ll be eligible for other business roles and not just IT roles.

    Start Small and Expand

    Ultimately, the goal with automation is to become autonomous. Therefore, it’s important to understand that not all tools are created equal. Many startups have entered the market with test automation tools, some of which make lofty promises about improved speed. But there is no tool that automates test scenario creation or provides intelligent test selection. What organizations can do is identify areas where it makes the most sense to start introducing automation and begin to implement it gradually.

    It’s key to remember that organizational support is imperative to successful deployment. Without it, projects aren’t likely to succeed. It’s easy to get stuck in the status quo because “that’s always how we’ve done it.” To move to automation there must be a desire for change. Being the change agent in the organization and finding support before embarking on this journey is crucial.

    Jumping Into the Automation Pool

    AI is rapidly becoming core to every industry and function, including the software industry. AI-based automation enables enterprises to take their software to market faster and reap unparalleled business benefits that come with agility, speed and reliability in their software release cycles. Software testing is only one application of this type of automation. Test automation not only relieves testers of the burden of repetitive tasks but can also perform additional testing that might not otherwise get done because it is difficult to do manually.

    Although the numbers clearly indicate that bugs and rework incur higher costs for organizations to maintain the status quo, many still hesitate to embrace the innovation. Most organizations agree that automated testing would be of tremendous benefit, and the hesitation indicates that early adopters will have a competitive advantage. With well-informed strategy and integration, test automation can enable you to deliver better software, faster and transform your business exponentially.

  • Going Through a Cloud Migration? AIOps Can Help

    Going Through a Cloud Migration? AIOps Can Help

    AIOps can help ease many of the challenges associated with cloud migration

    As businesses seek to compete in the digital economy and better engage with customers through new digital experiences, they have become increasingly reliant on cloud as a critical component of their digital transformation strategies. This has never been more true than during the COVID-19 pandemic, when businesses across all industries are aiming to provide the best digital experiences possible in our new, heavily remote world.

    Gartner validates this reliance on cloud in a recently published report, which states that although IT spending is set to decline in 2020 due to the impact of COVID-19, the pandemic’s increase in remote work has set public cloud services to grow 19% this year.

    There’s no doubt that cloud migration can be challenging, but properly preparing and utilizing tools such as AIOps can help initiate this process and can provide ongoing visibility.

    The Ingredient for a Successful Cloud Migration

    AIOps helps identify and resolve issues before they disrupt the end user during a cloud migration, arming technologists with insights and providing visibility within multi-cloud and containerized environments as companies manage their complex platforms.

    A surge of data can be managed in the cloud but can be difficult to manage without having visibility across the platform. AIOps offers an overview and provides updates and optimization suggestions, which helps businesses be competitive and have control.

    The AIOps and Cloud Migration Process

    AIOps exists to help with big IT challenges and there is a suggested path businesses can use pre-, during and post-migration.

    Pre-Migration

    Before the migration begins, you should measure the existing performance of your applications as a baseline. You can then define performance goals and find bottlenecks you should avoid during your cloud migration. You must evaluate the potential impact of your cloud migration on your IT environment, which AIOps can help with. With robust visibility, you can see results on applications that power your business.

    During Migration

    Once you have migrated your workloads to the cloud, you can utilize machine learning and AI to check which resources might be underused. AIOps on the cloud will provide insight on overused resources, so you can identify instances that are best for your company’s needs.

    Post-Migration

    Being able to monitor your services on the cloud and end user performance gives full visibility into what you are getting from your cloud provider. Needs can change over time, so a cloud migration strategy that incorporates AIOps can identify what can be altered. This functionality helps make it clear which applications could benefit from being moved.

    The reliance on cloud support is only increasing, so the sooner you incorporate AIOps into your IT environment, the faster you can have confidence your services will deliver improved performance for the end user. Why not get the most out of your investment in the cloud by using AIOps to support your business for the long term?

    AppDynamics is sponsoring KubeCon + CloudNativeCon 2020 North America, taking place Nov. 17-20. To learn more about how AppDynamics is supporting businesses in their cloud migration journeys, register for our webinar during the show here.

  • Means and Production

    Means and Production

  • Harness to Apply AI to DevOps

    Harness to Apply AI to DevOps

    Harness, at its {Unscripted} 2020 conference today, announced its plans in the fourth quarter to make available as a beta a module that leverages machine learning algorithms to optimize build and test cycles on the Harness Continuous Integration (CI) Enterprise platform.

    At the same time, Harness is adding a beta of a Continuous Features module to enable DevOps teams to employ feature flagging to simplify the application development process. A visual dashboard for feature verification and analytics monitors all flags, including active, inactive, live and unrequired flags, as well as customer issue tickets, to simplify the management of new capabilities being added to an application.

    Harness is also updating its continuous delivery (CD) platform to include a revamped user interface, GitOps and pipelines-as-code processes, bi-directional synchronization and conflict management, and templates to standardize deployments across teams.

    Finally, Harness is adding a Harness Next Generation Continuous Verification tool to provide visibility into the impact changes have on IT environments. This tool can be deployed standalone or used in conjunction with any CD platform and is intended to make it easier for DevOps teams to discover the root cause of issues that typically stem from recent changes made to an otherwise stable IT environment.

    Harness CEO Jyoti Bansal said artificial intelligence (AI) in the form of machine learning algorithms are poised to play a major role in taking DevOps automation to the next level. The Harness AI module can reduce test cycle time by up to 75% by correlating and isolating tests to changed code rather than requiring all tests to be executed with every change. The Harness approach means only relevant test cycles are run, he said.

    Continuous Integration Enterprise will also be able to identify gaps in test plans by highlighting code changes that are not covered by existing test cycles. Library caching and container image size optimization will also be added in the future to make builds more efficient.

    Harness also announced its intent to apply machine learning algorithms more broadly. Algorithms will enable DevOps teams to control, optimize and accelerate the flow of software pipelines rather than requiring engineers to manually verify builds, tests, deployments and releases. Harness will also apply data science techniques to observe the quality, performance, cost and reliability of code as it flows through each pipeline step and, if need be, automatically abort or roll back the delivery of software.

    As each build is committed, the opportunity to collect even more metadata about the workflow process only increases, Bansal added.

    Obviously, Harness is not the only provider of a CI/CD platform looking to apply machine learning algorithms. Thanks to machine learning algorithms, the rate at which applications are developed and deployed could increase by several orders of magnitude in the months ahead. The issue now is to what degree the rest of the business will absorb that potential rate of change.

  • Snyk Brings AI to DevSecOps

    Snyk Brings AI to DevSecOps

    Snyk today at its SnykCon 2020 conference announced a static application security testing (SAST) dubbed Snyk Code that incorporates an interpretable machine learning semantic code analysis engine the company gained through its acquisition of DeepCode earlier this year.

    The company also announced it has extended its alliance with Docker Inc. to become the exclusive provider of security insights for both Docker Official Images and future content certification programs from Docker. Available via Docker Hub, Docker Official Images are a collection of 166 curated container images that developers frequently reuse.

    At the same time, Snyk today announced it has integrated its vulnerability database with the IT monitoring service provided by Datadog as well as the IBM Cloud service.

    Snyk President Guy Podjardny said Snyk Code is aimed specifically at developers as part of an effort to advance the adoption of best DevSecOps practices. It provides visibility into application source code, open source libraries, container infrastructure and infrastructure as code to enable developers to better secure applications as they are being built.

    The company claims Snyk Code is up to 50 times faster than traditional SAST solutions, which would represent a major boost in productivity for developers who often don’t have the patience required to scan code as much as IT security teams would prefer.

    Snyk Code also curates machine learning models using the company’s curated vulnerability database to significantly reduce false positives. It also automatically models application programming interfaces (APIs) based on the best practices observed by the platform.

    Snyk Code is also designed to be integrated with CI/CD platforms to streamline DevSecOps workflows.

    Podjardny said Snyk went beyond simply productizing the Deep Code tool to incorporate capabilities that would specifically appeal to developers rather than cybersecurity teams. Given the volume of code that needs to be scanned before and after code is deployed in a production environment, Podjardny said it’s apparent that DevSecOps isn’t going to be achieved without augmenting developers with artificial intelligence (AI) tools.

    It’s not clear at what rate responsibility for IT security is shifting left toward developers. Beyond simply providing developers with the tools required to analyze code, workflows that DevOps and cybersecurity teams currently rely on must be better integrated. That’s a challenge, because the current rate at which DevOps teams are updating applications typically exceeds the ability of a limited team of cybersecurity professionals to keep pace. The days when cybersecurity teams could have weeks to review an application before being deployed are all but over.

    It may take a while for DevOps teams to gain confidence in AI tools. However, it’s clearly not feasible for human developers to identify every potential vulnerability within thousands of lines of code running on multiple platforms that can have any number of vulnerabilities which developers are not likely to know much about. As such, it’s not so much a question of if AI will play a major role in enabling DevSecOps as much as when.