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Tag: algorithms

  • How AI is Changing DevOps for the Better

    How AI is Changing DevOps for the Better

    Managing a DevOps environment involves a high degree of complexity. The rapid proliferation of data has made it difficult for DevOps teams to effectively onboard, ingest and unlock data and implement information to make good business decisions and deliver delightful experiences. Imagine a team poring over troves of information to identify critical factors that triggered an event—they would end up investing long hours to interpret and analyze the information.

    The future of DevOps will be AI-driven. It’s fair to say that it’s challenging for human intelligence to harness large volumes of highly complex data. Consequently, AI-powered solutions will become the number one choice for integrating and analyzing data and transforming how teams develop, deliver, deploy and manage applications.

    By 2023, 40% of DevOps teams will leverage application and infrastructure monitoring solutions with built-in AI capabilities, Gartner noted. Currently, in times of disruption, Gartner predicts the AIOps market will grow at breakneck speed, between $300 million and $500 million per year.

    Let us explore how AI and DevOps are intertwined before we explore how AI is transforming DevOps.

    DevOps and AI are Intertwined

    AI and DevOps are intertwined. Integrating AI in a DevOps environment boosts the overall performance and efficiency of an organization.

    AI and ML-powered solutions enable DevOps teams to onboard, map and integrate large volumes of customer or partner data with accuracy and speed. That is to say, business users find it easier to drive processes like data onboarding, data mapping and data integration, thus improving ease of doing business and delivering improved customer experience (CX).

    Organizations that rely on AI-powered solutions no longer need to devote long hours to custom coding and EDI mapping to analyze large volumes of data. The time saved can be used to focus on more important, innovation-driven tasks.

    AI also boosts automation, allows users to address and resolve issues in both data and applications and facilitates better cooperation among teams.

    4 Ways AI is Transforming DevOps

    The efficacy of DevOps correlates with an upward shift in AI. It can empower even non-technical business users to create connections and drive transactions at the speed of business. As a result, it brings non-technical business users to the forefront of business, thus freeing IT and developer teams to focus on more important tasks. Business users can leverage machine learning algorithms to collect data from different parts of DevOps systems and then map and integrate it in minutes through predictions.

    Here are four ways AI is transforming DevOps:

    1. Improved data access: The lack of free access to complex data streams is a challenge for DevOps teams. AI enables business users to access all the siloed data and harness its true potential for better insights delivery and decision-making. AI-powered tools can gather data from various sources for consistent and repeatable analysis.
    1. Faster data integration: AI and ML-enabled technologies enable non-technical business users to execute data mapping and data integration in minutes instead of months. As non-technical users drive operations, IT becomes free to take up governance and instead focus on more high-value tasks. Thus, not only can AI help business users integrate data faster to deliver experiences and value but also enables IT teams to drive innovation and growth.
    1. Higher implementation efficiency: With the support of AI, businesses can perform more tasks with less human intervention and minimal coding. This reduces the burden laid on IT or developer teams, enabling them to concentrate more on innovation and creativity.
    1. Better security: As security is essential to facilitate a successful software implementation, DevSecOps is a fundamental aspect of software development. Organizations must bolster their security systems to guard against an increase in threats. AI can play a vital role here. It can enhance DevSecOps and boost security by recording threats and executing ML-based anomaly detection through a central logging architecture. By combining AI and DevOps, business users can maximize performance and prevent breaches and thefts.

    Accelerating development cycles while making sure data is interpreted and analyzed properly is a challenge faced by DevOps teams today. Artificial intelligence-powered solutions are helping organizations transform every stage of DevOps building cycles without burdening IT teams or adding costs. Ergo, AI can enable users to transform the DevOps environment, deliver delightful experiences and grow revenue.

  • Mona Allies with New Relic to Converge MLOps and DevOps

    Mona Allies with New Relic to Converge MLOps and DevOps

    New Relic and Mona, a provider of a platform for monitoring the models used to provide artificial intelligence (AI), announced today they have formed an alliance to help bridge the divide between DevOps teams and data scientists.

    Most data science teams today build models based on machine learning algorithms using operations platforms that enable them to manage the life cycle of those models, otherwise known as MLOps. Mona provides a set of monitoring tools that are employed to detect drift that can occur as some of the underlying assumptions that were made when building the model are discovered to be less accurate or when new data sources become available.

    New Relic, meanwhile, provides an observability platform that is widely employed by DevOps teams to monitor and observe an IT environment. IT teams that have access to New Relic One can now also view dashboards provided by Mona that detect anomalies involving data integrity issues and model performance issues.

    The integration also enables users to create custom Mona alerts that are shared via the New Relic Alerts and Applied Intelligence service. That capability reduced the volume of alerts generated by correlating anomaly detection to specific IT events.

    Yotam Oren, CEO of Mona, said that level of integration provides a bridge between DevOps and data science teams that typically have widely divergent cultures.

    Incorporating AI models within applications is the next major DevOps challenge. There is almost no application being built today that won’t incorporate some level of AI. The challenge is AI models are built by data scientists that employ a wide range of machine learning operations (MLOps) platforms to construct those models. The pace at which those AI models are developed, deployed and updated varies widely. However, all organizations will eventually need to confront the problem of how to insert AI models within applications both before and after they are deployed in a production environment.

    There are two schools of thought about how best to achieve that goal. The first is to assume an AI model is just another type of software artifact that can be managed as part of an existing DevOps process. In that context, there may be no need for a separate MLOps platform. Conversely, proponents of MLOps contend AI models are built using pipelines created by data engineering teams working in collaboration with data scientists. Developers only become part of the process when an AI model needs to be inserted into an application environment. AI models may ultimately need to be incorporated within an application, but the proponents of MLOps platforms maintain building models requires a unique platform to manage a software artifact that is built by training algorithms.

    It’s only a matter of time before compliance issues force some form of convergence between DevOps and MLOps teams, noted Oren. The longer it takes to address both the cultural and technical issues that are required to meld DevOps and data science teams, the more challenging it will become as both teams are set in their respective ways.

  • RiskRecon Invents Ground-Breaking Asset Risk Valuation Algorithms, Transforming How Enterprises Manage Third-Party Cyber Risk

    New algorithms solve the cyber risk equation by automatically determining the risk value of computer systems, enabling precise cyber risk assessment and action

    Salt Lake City, Utah — October 15,  2018 — RiskRecon, the world’s leading platform provider for easily understanding and acting on third-party cyber risk, announced its ground-breaking asset valuation algorithms that automatically determine the inherent risk value of any Internet-facing system. Automatically determining asset value is critical to managing cyber risk because it enables organizations to easily create action plans focused on addressing risk.

    “Risk professionals spend too much time analyzing mountains of issues to determine the risk relevance,” explained Kelly White, Co-Founder and CEO of RiskRecon. “RiskRecon automatically contextualizes every issue with issue severity and asset value that enables professionals to easily identify risk priorities and needed action.”

    RiskRecon visually summarizes issue risk priority within a “Risk Prioritization Matrix,” showing each issue within the context of issue severity and asset risk value. Summarizing the risk priority of 3,000,000 issues existing in commercial Internet-facing systems reveals that only 0.12% are critical severity issues in high-value assets. “The vast majority of risk resides in less than 6% of total issues,” explained White. “RiskRecon enables you to easily identify the issues of risk that matter and, just as importantly, identify the issues that don’t.”

    Jack Jones, Chairman of the FAIR Institute and Co-founder of RiskLens, noted that: “Far too much energy in information security is wasted on resolving issues that don’t matter. As the FAIR model promotes, effective risk management requires understanding the probable frequency and magnitude of loss; that depends on understanding asset value. I am really pleased to see RiskRecon bring the ability to automatically determine asset value to market.”

    RiskRecon’s asset valuation algorithms automatically assign a value to cyber assets such as systems, domains, and networks. The algorithms also tag each asset with value indicators, including the system’s functionality and the data types it collects; these indicators enable risk professionals to immediately understand any asset’s value.

    Deployed to third-party risk management, RiskRecon’s automated risk assessments provide the most precise visibility into vendor cyber risk performance, enabling dramatically better third-party risk outcomes with much greater efficiency.

    RiskRecon customers use this capability to better solve third-party cyber risk, enabling them to identify and act on the vendors and issues that expose them to the greatest risk. Organizations also leverage RiskRecon to better understand their own risk surface and exposures.

    About RiskRecon

    RiskRecon is the only continuous vendor monitoring solution that delivers risk-prioritized action plans custom-tuned to match your risk priorities, providing the world’s easiest path to understanding and acting on third-party cyber risk. Partner with RiskRecon to build your scalable, third-party risk management program to realize dramatically better risk outcomes. To learn more about RiskRecon’s approach, request a demo or visit the website at www.riskrecon.com.

    Connect with RiskRecon:

    LinkedIn: https://www.linkedin.com/company/riskrecon/

  • GitHub Applies Data Science to Managing Code

    GitHub Applies Data Science to Managing Code

    Over the last several years GitHub has emerged as one of the primary repositories around which application development now revolves. At its recent GitHub Universe 2017 conference, the company revealed how it is extending that central role to provide DevOps teams with the addition of a dependency graph that can be employed to alert DevOps teams when a module of code has been updated and a security alert service that will notify them anytime a patch has been made available for a specific module.

    Miju Han, engineering manager for data science for GitHub, says both new capabilities are examples of how the company is applying advanced algorithms and data science techniques to make it easier to manage DevOps processes that revolve around GitHub.

    In addition, Han says GitHub is making available a news feed through which DevOps teams can track updates to groups of modules stored in the repository, as well as a tool through which they can explore a curated set of modules.

    Han says his company intends to combine a variety of emerging data science tools to simplify the daily workflow that revolves around GitHub. As the repository has become more widely used, the sheer volume of code and related updates a DevOps team is supposed to track has become, in many cases, overwhelming. That’s why the company is now pouring resources in several data science research projects with an eye toward making it simpler to navigate the repository.

    The latest annual edition of an Octoverse report published by GitHub finds that there are now more than 24 million developers in 1.5 million organizations around the world accessing 67 million GitHub repositories. A total of 25 million of those repositories are public. The most popular programming languages in use across GitHub are JavaScript (2.3 million projects); Python (1 million projects); Java (986,000 projects) and Ruby (870,000 projects). In terms of pull requests, GitHub notes that Python surpassed Java in popularity in the last year. The company also notes that half of the largest companies in the U.S. based on revenue are employing GitHub repositories.

    There is no shortage of repositories being employed inside and out of enterprise IT organizations, including an enterprise edition of GitHub that the company claims has been implemented by 45 percent of the Fortune 100 list of the world’s largest companies.

    Arguably, the biggest challenging many organizations now face is trying to reconcile the various code repositories in use. Just about every provider of an application development platform provides access to a code repository. In addition, providers of everything from container frameworks to application lifecycle management (ALM) often provide similar capabilities. Keeping track of what version of any given piece of code resides where at any given time has become a significant challenge.

    The good news is that providers of repositories are starting to address that issue on their own platforms, which means it stands to reason that one day soon tools for managing code across heterogeneous repositories may not be all the far off. In the meantime, DevOps teams can take some comfort in the fact that the management tools they rely to manage all the code are about to become a whole lot smarter.

    — Mike Vizard