devopsy.kinsta.cloud

Author: Mike Vizard

  • Postman Survey Shows Focus on Building and Deploying APIs

    Postman Survey Shows Focus on Building and Deploying APIs

    A survey of more than 28,000 developers and application programming interface (API) professionals published today by Postman found nearly half of respondents (49%) are now spending the majority of their time developing APIs. More than a quarter (28%) now spend 20 or more hours per week working with APIs.

    Kin Lane, chief evangelist for Postman, a provider of a platform for building APIs, said that shift reflects an increase in software development and integration activity that began at the start of the COVID-19 pandemic. Almost two-thirds of respondents (63%) said the pandemic somewhat or significantly affected their work, with almost half of those respondents noting that changes made with their organization were enabled and supported by APIs.

    Additionally, a full 94% of respondents said the investment of time and resources into APIs will increase or stay the same for the next 12 months. The biggest obstacle to creating APIs is lack of time (45%) while the biggest obstacle to consuming an API is lack of documentation (55%). In terms of API goals, the survey found that quality (80%), agility (66%) and reliability (65%) are the top three goals among survey respondents.

    However, the survey also noted only a third of respondents said they could build and deploy an API in anywhere from a single day to a week, with most requiring one week to a month. About one-third also said they deploy APIs to production between once per week and once per month. More than 80% of respondents noted that less than 25% of their changes fail while a similar percentage said they can recover in less than a day with a third reporting they fully recover in less than an hour.

    The Postman survey also found that more than three-quarters of respondents (76%) have less than five years of experience with API development. Only half of respondents indicated that they agree or strongly agree that the leadership of their API team has a clear sense of where their API practice should be in five years. However, microservices (58%) and Kubernetes (50%), containers (46%), serverless architecture (44%) and GraphQL (35%) were ranked among the technologies that respondents were most excited about heading into 2022.

    On the plus side, more than 60% of respondents felt their API leadership and governance teams demonstrated a “willingness to change” as well as duly considering “the interests and opinions of employees.” When it comes to regularly providing feedback and process, more than half of leadership and governance teams (53%) scored well.

    The top two ways API teams collaborate is via a dedicated collaboration platform (50%) or by publishing API artifacts to repositories such as GitLab, GitHub and Bitbucket (45%), according to the Postman survey. The most widely used DevOps tools are Jenkins (37%), Azure DevOps (26%), GitHub Actions (25%), AWS DevOps (23%) and GitLab Pipelines (22%).

    In terms of deploying APIs, continuous integration/continuous delivery (CI/CD) platforms are the most widely employed (57%), followed by deploying APIs in the cloud (38%), frameworks (34%) and custom-built deployment methods (27%).

    Just over 70% of respondents ranked security and performance as the top factors considered when integrating with an API. When producing an API, however, the top factors are integration with internal systems, integration with external systems, adding functionality to an internal system and enhancing functionality for an end customer. Similarly, when consuming an API the top factors are integration with external systems, adding functionality for end customers, adding functionality to internal systems and integration between internal systems.

    In general, Lane noted that, as more APIs are developed and deployed to drive various digital business transformation initiatives, organizations will need to pay more attention to API discovery and management. There’s a tendency for different teams within organizations to not only create redundant APIs but also forget about so-called zombie APIs that are no longer supported but can still be exploited by cybercriminals.

    Overall, Lane said the survey makes it clear more organizations are pursuing an API-first approach to digital transformation. The challenge now is managing all the APIs that can quickly become too much of a good thing.

  • Intel Woos DevOps Teams with API and Processor Advances

    Intel Woos DevOps Teams with API and Processor Advances

    During the online Intel Innovation 2021 event, Intel announced today a unified Developer Zone for accessing application programming interfaces (APIs) that it makes available via a oneAPI toolkit to IT teams that want to more easily invoke a wide range of processor types and classes.

    At the same time, the company also revealed it is creating a set of oneAPI centers of excellence that will make it easier for DevOps teams to consume those APIs. Partners participating in that initiative include Oak Ridge National Laboratory, University of California Berkeley, University of Durham and University of Tennessee. Intel will also make the Intel Graphics Visualization Institutes of Xellence part of this program.

    Next year, Intel is also promising to deliver oneAPI 2022 toolkits that will add 900 additional capabilities including an ability to develop applications for CPUs and graphical processor units using what Intel described as the first unified C++/SYCL/Fortran compiler.

    The company is also planning on making available Data Parallel Python tools in addition to expanding its Advisor accelerator performance modeling tools to include support for VTune Flame Graph to visualize performance hot spots.

    There will also be additional oneAPI integrations to add support for Microsoft Visual Studio Code and the Microsoft Windows Subsystem for Linux 2 platform. Intel also created an Intel oneAPI AI Analytics toolkit to enable out-of-the-box AI development that is supported by workstations running Linux from Dell Technologies, HP and Lenovo. Additionally, Microsoft and Intel have agreed to create a complete data science toolchain to Windows 11 that will become part of the Surface Laptop Studio framework for building applications.

    Greg Lavender, chief technology officer, senior vice president and general manager of the software and advanced technology group at Intel, said it’s critical to make sure that DevOps teams add support for the acceleration engine and extensions that Intel makes available within its processors to the runtime environments they deploy. Otherwise, developers will never be able to routinely invoke those capabilities to build next-generation applications.

    The company also revealed that next-generation Intel Xeon Scalable Processors, code-named Sapphire Rapids, will have additional acceleration engines to both simplify deployments at scale and optimize processors’ core utilization in a way that reduces power consumption. Overall, Intel is promising up to a 30x total performance gain for artificial intelligence (AI) applications running on these processors.

    Other hardware advances announced today include a 12th generation Intel Core family based on 7-nanometer processors and a Mount Evans project through which Intel is collaborating with Google Cloud to build processors based on application-specific integrated circuit (ASIC) chips.

    The company also unveiled an Intel ArcTM Alchemist family of graphics systems on chips (SoCs) that take advantage of machine learning algorithms to optimize performance and touted an Intel Tofino fabric processor (IFP) that adds intelligence to network switching via support for P4 programming tools.

    Finally, the company noted that an Aurora supercomputer, due out in 2022, will exceed two exaflops of peak double-precision compute performance.

    As Intel seeks to regain the processor high ground from rivals such as NVIDIA, ARM and AMD, it’s clear that the future of IT will be defined by a broad range of processor classes that all have unique capabilities. The challenge now is deciding which of those capabilities to actually make available to developers that are largely dependent on the runtime environment that DevOps teams decide to make available.

  • GitHub Expands Developer Productivity Tools Portfolio

    GitHub Expands Developer Productivity Tools Portfolio

    During the online GitHub Universe 2021 conference, GitHub today unveiled a bevy of updates intended to boost developer productivity by making it easier for developers to write code faster and collaborate more easily.

    The GitHub Copilot tool that GitHub developed in collaboration with OpenAI, a research and development lab, to help developers write better code faster is being extended via a technical preview to add support for Java and the integrated development environment (IDE) JetBrains.

    In addition, the Codespaces tools that GitHib makes available to pre-configure development environments is being extended to include support for devcontainer feature composition, the GitHub command line interface (CLI) and REST application programming interfaces (APIs) along with additional access controls.

    GitHub Actions has also been updated to add support for deployments using the OpenID Connect standard in addition to providing access to deployment environments to simplify approvals, improvements to reusable workflows and auto-scaling functionality for self-hosted runners.

    There is also now a Command palette tool in beta to help developers navigate GitHub along with a public beta of a GitHub Issues tool that makes it simpler for developers to filter, sort and group issues and pull requests. Additional features that have been added since GitHub Issues was first previewed include iteration support, reporting and data visualization tools and the ability to employ it across public projects.

    Finally, GitHub is adding, in beta, support for Ruby as a programming language that CodeQL analysis engine can now scan for vulnerabilities along with the ability to customize permissions for accessing GitHub Enterprise Cloud.

    Ryan Salva, vice president of product management for GitHub, said collectively GitHub is committed to making developers more productive by, for example, autocompleting code as developers write. Support for additional programming languages will also be forthcoming in the months ahead. The goal is not to leverage AI to replace developers but, rather, eliminate the drudgery that gets in the way of developers becoming more creative, noted Salva.

    There won’t come a day any time soon when developers won’t have to type to write code, but it is possible to leverage AI to provide a type-ahead capability that reduces coding errors, added Salva.

    The goal should be to enable developers to create more application experiences at a time when more organizations than ever are consuming applications that drive a wide range of digital business process transformation initiatives, said Salva.

    As part of that effort, Salva noted it’s also critical to reduce the amount of friction developers encounter writing code starting with setting up the development environment itself.

    It’s not clear yet what impact all the focus on developer productivity and collaboration will have on the rate at which applications are being built and deployed. However, as developers become more productive, the number of application development projects that can be launched simultaneously should increase. In fact, there may come a day when applications are being built faster than organizations can deploy them without adopting continuous delivery best practices that rely on higher levels of automation to install software.

    Regardless of that application delivery challenge, however, it’s a problem most organizations right now would consider themselves fortunate to have.

  • BMC Looks to Advance ServiceOps Approach to Managing IT

    BMC Looks to Advance ServiceOps Approach to Managing IT

    During an online BMC Exchange event, BMC announced it expanded its portfolio of IT management offerings as part of an ongoing effort to unify traditional IT service management and DevOps processes under an integrated ServiceOps framework.

    The latest additions to the BMC portfolio includes an update to its Control-M workload automation platform that adds observability tools for monitoring application and data workflows along with additional integrations for cloud services such as AWS Glue, Google Cloud Dataflow and Function and Microsoft Azure Data Factory and platforms such the data lake from Databricks.

    BMC is also making available a preview of a Control M Python client that will extend the reach of the workload automation platform to the code data science teams employ to build artificial intelligence (AI) models.

    In addition, BMC is adding a BMC Helix Digital Workplace studio to make it easier for IT teams to customize the software-as-a-service (SaaS) platform it provides for IT service management (ITSM). Finally, BMC is also making available a version of BMC Helix that is optimized for communications service providers.

    BMC CEO Ayman Sayed said overall the current emphasis on digital business transformation is driving a need to converge the management of multiple IT platforms as organizations reinvent how core processes work across their organization. It’s not possible to drive that level of reengineering across multiple IT platforms without centralizing the management of those platforms using a ServiceOps framework that unifies DevOps and existing ITSM platforms, he noted.

    At the same time, Sayed said organizations need to invest more in artificial intelligence to automate IT operations, also known as AIOps, to augment IT staff that is hard-pressed by the need to manage both emerging and legacy IT platforms. The latest investment BMC has made in AIOps came earlier this month with the acquisition of StreamWeaver, a provider of a set of tools that make it easier to collect massive amounts of data that can be used to more easily train AI models.

    It’s too early to say to what degree IT management processes might become unified. In addition to centralized IT teams, most organizations have business units that often drive their own IT initiatives. However, as organizations realize how many platforms a digital process needs to access the need to centralize the management of IT becomes more apparent. The issue each organization will need to determine is the degree to which that centralization needs to occur.

    It’s clear in the wake of the COVID-19 pandemic that many IT organizations have never been more agile. In some cases, those IT teams are now able to drive a rate of change that is too fast for many businesses to even absorb. In fact, it’s arguable that entire businesses will need to adopt DevOps principles to manage that rate of change.

    In the meantime, IT teams will need to take a harder look at how much time is being spent on manual tasks that could be automated in a way that enables them to be lot more productive than they are today.

  • Sysdig Adds Cloud Access Controls to DevSecOps Platform

    Sysdig Adds Cloud Access Controls to DevSecOps Platform

    Sysdig announced today that it is adding a Cloud Infrastructure Entitlements Management (CIEM) capability to its Secure DevOps platform as part of an effort to better enforce least-privilege access within the context of a larger zero-trust approach to cybersecurity.

    Nick Fisher, vice president of product marketing for Sysdig, said this addition to the company’s core DevSecOps platform for securing cloud services will prevent organizations from inadvertently providing individuals with excessive permissions that result in cybercriminals gaining widespread access to applications and systems when the credentials those individuals have are compromised.

    A recent report from the Sysdig Threat Team found that nearly 80% of users have excessive entitlements due to overly permissive policies. Similarly, Gartner forecasted that 75% of security failures will result from inadequate management of identities, access and privileges by 2023, up from 50% last year.

    The CIEM offering from Sysdig enables cybersecurity and IT operations teams to resolve that issue across multiple clouds in under two minutes once installed, said Fisher. The Sysdig platform achieves that goal by making it possible to leverage templates as a set of infrastructure-as-code (IaC) tools that enable DevOps teams to integrate security within their existing workflows.

    CIEM also simplifies access control auditing by making it easier to discover who in the organization has permission to access cloud infrastructure services. As part of that capability, Sysdig also provides out-of-the-box compliance policies and access to reports that can be generated on-demand to surface a detailed audit trail of all cloud permission changes.

    Finally, CIEM will generate an alert any time someone is denied permission to access a cloud resource, noted Fisher. That capability provides cybersecurity teams with an opportunity to then review what permissions might need to be granted or whether a bad actor is attempting to compromise a cloud environment.

    Most of the cloud security issues that organizations encounter can be traced back to developers that have misconfigured one cloud resource in a way that allows cybercriminals to access, for example, a port that has been left open. CIEM extends the Sysdig platform so that, in addition to scanning for vulnerabilities and misconfigurations, it can detect and respond to attacks across containers and virtual machines deployed in a cloud environment.

    Thanks, in part, to recent high-profile breaches of software supply chains, there’s a lot more focus on cloud security as the rate at which applications are being deployed on these platforms steadily increases. While the cloud platforms themselves are fundamentally secure, the challenge most organizations are encountering is they have immature processes in place for securing the workloads running in the cloud.

    Of course, being able to limit access to those cloud resources is only the first step toward implementing a set of DevSecOps best practices to ensure the integrity of the cloud platforms being employed. Unfortunately, too many developers still tend to assume the cloud service provider is taking steps to secure workloads on their behalf. In reality, cloud service providers have adopted a shared responsibility model that, from their perspective, limits their cybersecurity responsibility to the core infrastructure they provide. It’s up to the DevOps team responsible for the workloads running on those platforms to figure out how to secure the rest of that IT environment.

  • Codenotary Uses Immutable Database to Verify Software Artifacts

    Codenotary Uses Immutable Database to Verify Software Artifacts

    Codenotary today unfurled a free notarization and verification service for open source artifacts and containers to enable IT organizations to track the provenance of the components that make up their applications.

    Dennis Zimmer, Codenotary CTO, said the Community Attestation Service is based on an immutable open source immudb database that cryptographically attaches an identity to each software artifact. Additionally, the Community Attestation Service can be integrated with the continuous integration/continuous delivery (CI/CD) platform that DevOps teams employ to build and deploy applications using those artifacts, added Zimmer.

    The identities created by the Community Attestation Service can then be used to verify a software bill of materials (SBOM) that will soon be required by not only every U.S. federal agency but also organizations that are trying to better secure their software supply chains in the wake of a series of high-profile breaches, said Zimmer. The Community Attestation Service will also encourage developers to reuse source code, builds, repositories and Docker container images that they know they can reliably employ within a zero-trust IT environment, noted Zimmer.

    Some developers have, of course, been signing their code for years, while others simply didn’t see the need. With its free service, Codenotary is trying to expand the pool of developers that will not only sign their code but do so in a way that can’t be tampered with later, noted Zimmer.

    Unlike blockchain databases that are employed to provide immutable records of transactions, Zimmer said the immudb platform was created to provide an immutable database capable of processing millions of transactions per second. That database is also being used to build a wide range of applications that go beyond the scope of the Community Attestation Service so DevOps teams can have confidence in the viability of the underlying platform, noted Zimmer.

    In the wake of some troubling security breaches, it’s now only a matter of time before security teams start to ask more difficult questions about how applications are built. Developers could wait for those cybersecurity teams to eventually create policies that DevOps teams would be required to enforce, or they can more proactively embrace a service that tracks the provenance of code that hopefully, has been scanned for all known vulnerabilities. In fact, it may not be too long before organizations decide not to employ code that has not been verified. There may even be a backlash against using code that is unsigned that could result in many existing applications either being updated or outright replaced.

    On the plus side, Zimmer noted there’s a lot more awareness of the need to sign code. The challenge is most organizations don’t have the processes in place. Fortunately, there’s a lot more interest in DevSecOps best practices to ensure the integrity of code. However, awareness of the need for something doesn’t always result in concrete actions being taken unless DevOps teams can simply make it easy for developers to do the right thing. After all, it’s not as though developers want to employ insecure code, it’s just that they are often simply too busy to make sure every last artifact employed is verified as secure.

  • CircleCI Adds Test Insights Module to CI/CD Platform

    CircleCI Adds Test Insights Module to CI/CD Platform

    CircleCI today announced it has added a Test Insights capability to its continuous integration/continuous delivery (CI/CD) platform to make it easier to identify tests that are either taking too long to run or simply exhibiting some form of anomalous behavior.

    Dawit Gebregziabher, product manager for data insights at CircleCI, said Test Insights is designed to make it simpler for IT organizations to shift more responsibility for testing further left toward DevOps teams that identify issues as applications are being built on the CircleCI platform.

    Each organization will still need to determine what tools to employ to create a test, but once it starts running, the Test Insights module will be able to surface the tests that, for one reason or another, are problematic to execute, he added.

    Test Insights, for example, will flag flaky tests and identify the longest-running and most failed tests. It will also enable DevOps teams to identify performance changes over time to pinpoint changes in application behavior.

    Finally, DevOps teams can track benchmarks at the organization, workflow and job level, as well as metrics for mean-time-to-recovery, throughput and durations for 24-hour, 7-day and 30-day windows of time.

    The challenge DevOps teams often encounter is a test may be run even when it’s apparent the application will fail. Test Insights makes it feasible for DevOps teams to manage the testing process much more efficiently, said Gebregziabher.

    It’s not quite clear how far testing is shifting left. There’s no doubt more developers are testing their code before it is loaded into a production environment to hopefully avoid issues that will only become more expensive to fix later. Other organizations have dedicated software engineers to create testing processes that are tightly integrated with CI/CD pipelines, noted Gebregziabher.

    However, many large enterprise organizations continue to also hire and retain dedicated application testing teams that are trained to identify issues that might either adversely affect the end-user experience or run afoul of compliance requirements.

    Regardless of who does what kind of testing, however, it’s clear there’s a lot more focus on addressing issues earlier when they are much less expensive to fix. The goal should be to make it easier to optimize when tests should run based on probabilities that ultimately reduce the time wasted running tests that either will fail or are, for one reason or another, flawed.

    Developers, of course, are all equally committed to running tests, so organizations will need to determine for themselves how far to shift that process left. However, at the very least, DevOps teams should be able to identify patterns that might indicate there is a need for additional developer training. After all, it’s unlikely a developer is going to understand there’s an issue until some meaningful metrics are shared.

    In the meantime, the more testing that occurs at a faster rate the more likely it becomes that an organization can provide a superior application experience the first time—and every time—an application is built and deployed.

  • InfluxData Simplifies AppDev for Time-Series Databases

    InfluxData Simplifies AppDev for Time-Series Databases

    During an online InfluxDays North America 2021 Virtual Experience conference, InfluxData today expanded the capabilities of its application development tools using its Flux query language on top of its time-series database.

    The company has enhanced its InfluxDB Notebooks tool to enable developers to quickly set up an alert, build a task or write a Flux script without having to leave their browser.

    In addition, developers can compose Flux queries to be included in client code that can then be autogenerated within applications written in more than 10 different programming languages. There is now also an extension of Visual Studio Code that makes it simpler to create Flux scripts, query InfluxDB, create Flux tasks and manage buckets.

    Finally, InfluxData added the ability to explicitly define the schema of a bucket within InfluxDB to safeguard against unwanted changes; an application programming interface (API) tool, dubbed API Invocable Scripts, to make it simpler to define parameterized Flux scripts and the ability to send notifications from Alerta, WebEx Teams and ServiceNow to the InfluxDB Cloud platform.

    Tim Hall, vice president of product for InfluxData, said the overall goal is to reduce the level of complexity developers encounter when building applications that run on a time-series database. Many of those applications are now core to digital business transformation initiatives that require data to processed and analyzed over time. In effect, time-stamped data adds yet another dimension to any application, noted Hall.

    Time-series databases are typically employed by either an independent software vendor (ISV) to create, for example, a monitoring application, or an enterprise IT team that is building an application that lends itself to data that needs to be tracked over some period of time.

    In most instances, applications built on a time-series database are being deployed alongside, for example, relational databases that are generally invoked using SQL. As a result, InfluxDB has made it simpler to join data across relational and time-series data via SQL integrations the company added to Flux, noted Hall.

    DevOps teams, of course, are not always anxious to add another database they need to support, but as applications continue to evolve, more organizations are employing a mix of purpose-built databases to develop applications as the volume of data that organizations are trying to manage and analyze continues to grow.

    In the longer term, it’s clear time-series databases will play a critical role in driving digital business transformation initiatives. The challenge most organizations are now struggling with is determining which of these initiatives to prioritize. A time-series database coupled with the developer tools provided by InfluxData will enable IT teams to build the applications that drive those processes much faster, said Hall.

    In the meantime, IT teams would be well advised to at least become more familiar with time-series databases, because knowing the time when an event occurred is becoming a bigger application requirement. After all, knowing when something happened is now just as important as the record of what actually occurred.

  • Eclipse Foundation Launches Open Source Edge Computing Initiative

    Eclipse Foundation Launches Open Source Edge Computing Initiative

    The Eclipse Foundation in collaboration with the OpenAtom Foundation today launched the Oniro project and working group to create an independent implementation of OpenHarmony, an open source operating system.

    Created by the OpenAtom Foundation, the OpenHarmony operating system is based on a HarmonyOS originally created by Huawei. It is designed to support multiple kernels but uses the Linux kernel if a device has a large amount of memory.

    It also includes a DSoftBus capability for integrating physically separate devices into a single logical super device that enables one device to control others in addition to facilitating data sharing among distributed devices.

    Huawei, in collaboration with Linaro, Seco, Array, NOITechPark and Synesthesia, has also been contributing to a continuous integration/continuous delivery (CI/CD) platform that is part of the larger Oniro project.

    Mike Milinkovich, executive director of the Eclipse Foundation, said the goal is to provide a common layer of abstraction platform that can be employed across a wide range of embedded systems that are typically being deployed at the network edge. That level of fragmentation surrounding platforms that don’t provide a lot of significant differentiated value only results in slowing down the deployment of applications, noted Milinkovich.

    As part of that effort, a dedicated working group has been created to provide a vendor-neutral structure through which contributions to the project can be made with greater transparency. The OpenAtom Foundation was created in Beijing last year with the support of Alibaba, Baidu, Huawei, Inspur, Qihoo, Tencent and China Merchants Bank. The alliance with the Eclipse Foundation brings more traditional governance rules for building open source software to the OpenHarmony project.

    In general, DevOps teams should be tracking the development of proposed de facto standards for edge computing platforms closely. More applications are now being deployed on edge computing platforms that make it possible to process and analyze data at the point where it is being created and consumed. The DevOps challenges associated with building and deploying applications across a highly distributed computing environment are considerable.

    In many cases, organizations that never really attempted or mastered continuous delivery of applications and their associated updates will be sorely challenged when it comes times to support hundreds, possibly thousands of applications, running on embedded systems based on a wide range of processors.

    In addition, many of those same DevOps teams will start to see traditional batch-oriented applications being superseded by applications based on event-driven architectures that process data in near-real-time. In most cases, those applications are going to be built on microservices-based applications that are typically more challenging to build and deploy than a traditional monolithic application.

    Arguably, IT organizations are on the cusp of a new era of IT that assumes most organizations have not only mastered DevOps best practices but are now also embracing DevSecOps best practices to secure those application environments.

    It may be a while before the open source software that will be developed by the Orino project finds its way into production environments, but it should be apparent that reducing complexity at the network edge should be a high priority for all DevOps teams heading into 2022.

  • 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.