Category: DevOps Culture

  • Improving DevX Gives Your Business a Boost

    Improving DevX Gives Your Business a Boost

    As organizations transform and grow, different methodologies and development tools can come together, creating an extra level of complexity for your mainframe application development teams. In a perfect world, developers would be able to focus on delivering solutions without worrying about all the infrastructure necessary to make that happen. Building a better developer experience, or DevX, can make that a reality.

    DevX is about meeting developers where they are, at every stage of their career, delivering compatibility with and connectivity to the tools and devices that they want to use so they can focus on what they do best: Coding and releasing high-quality mainframe applications faster and more frequently and fostering better productivity and more innovation.

    Automation is integral to removing friction for developers. A modern integrated development environment (IDE) with built-in automation allows developers to find and manage code more efficiently and with greater quality and move quickly and easily from coding to editing to debugging to testing.

    Modern tooling can simplify and accelerate how developers work by giving them a visual representation of their code layout in a single-pane-of-glass view. They can move quickly in a natural, easy way with more information at their fingertips while built-in automation offers assistance at the right moment without getting in the way.

    This can seem difficult when dealing with mainframe application development and longtime mainframe developers who are still working with tools from the 1980s haven’t directly benefitted from the intervening decades of improvements. What they might not realize is that the improvements made for other platforms are available to mainframe developers, too. By engaging them in your DevX modernization efforts, you can improve their experience—and that of their team members.

    Elements of a Productive Mainframe Developer Experience

    • A modern DevX adopts flexible agile processes that give developers predictable capabilities and enable smaller, iterative changes with faster feedback. It also syncs distributed and mainframe development teams to foster greater collaboration for additional process improvements.
    • A better DevX is platform-agnostic, creating a development environment where developers can focus on the functionality of the applications they’re building instead of the deployment mechanism, leveling the playing field for every generation of developer.
    • With an improved DevX, developers can achieve the full potential of DevOps. Implementing DevOps for source code management (SCM) functions allows developers to understand the scope of any changes needed before they start coding, which helps speed approvals through concurrent work streams.
    • The single biggest boost to productivity—and an integral component of DevX—is shift left automated testing that enables developers at all skill levels to test new code immediately and find and fix bugs earlier in the development cycle when they’re easier to fix—and before they reach the end user. Continuous testing also automatically prepares test data within the DevOps toolchain.
    • An improved DevX delivers operational insights, so developers aren’t just catching issues but also understanding and prioritizing them, working hand in hand with the operations team to make the overall system better. It also empowers newer developers to address and correct any issues that arise in legacy code without poring over hundreds of modules to find what they need to improve. DevX tools work together to give developers an opportunity to leverage operational data in a whole new way.

    Once you have the tools, your mainframe developers need to be supported by a culture and education that teaches them how to use them and understand the benefits they offer. If you implement new tooling that nobody will use, the transformation is for naught. It’s important to start with the user experience and find the right tools to address it rather than starting with a tool and forcing its use just because it’s new. Continuous improvement is an important part of this; otherwise, you could bring the mainframe developer experience from the 1980s up to today’s standards only to have it freeze in place. This is why metrics and analytics are so important.

    As you speed up delivery, you also want to make sure the quality improves. With an improved DevX, you’re consistently finding defects earlier in the life cycle, which results in safer, more efficient delivery of better-quality code. By tracking velocity with metrics such as user stories delivered in one sprint, you can gauge how those changes are making a difference. You can also demonstrate how automation helps eliminate human error, increases code drops, and improves business agility.

    If you’re giving your mainframe developers everything they need to do their jobs easily and more efficiently, they’re exponentially more likely to adopt the tool and advocate for it—because a better DevX improves the day-to-day employee experience for longstanding and next-generation developers. By removing the drudgery of tasks that take developers away from writing code, you’re unleashing their creative and enabling them to achieve even greater levels of innovation—and you’re also improving your hiring and retention practices.

    Improving the mainframe DevX pays dividends across your business, from your developers to your customers to your stakeholders, so you can bring more new features to market faster and enable a competitive response for revenue-generating solutions. The mainframe is here to stay. Make sure you’re building a modern development environment that empowers your developers to respond nimbly in the face of change and make change your advantage.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

    Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

    Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

    The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

    We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

    LinkedIn

    Samsung Electronics America
    Mountain View, California
    Lead Site Reliability Engineer (SRE) – DevOps Specialist
    $180,000 to $210,000

    Indeed.com

    Federal Reserve Bank of New York
    New York, New York
    DevOps Engineer
    $178,100 to $207,900

    CareerBuilder.com

    SAIC
    Chantilly, Virginia
    DevOps Advisor
    $130,000 to $200,000

    Dice.com

    Saicon Consultants
    Alpharetta, Georgia
    DevOps Engineer
    $110,000 to $140,000

    Simply Hired.com

    Hewlett-Packard Enterprise
    Fort Collins, Colorado
    DevOps Automation Engineer
    $109,400 – $251,500

  • How to Secure C-Suite Buy-In for Your DevOps Efforts

    How to Secure C-Suite Buy-In for Your DevOps Efforts

    You could be forgiven for assuming DevOps is the modus operandi only for big modern enterprises like Tesla and Uber, but this is not the case. DevOps builds on earlier approaches, such as Agile development and Lean methodology, by emphasizing the importance of collaboration between development and operations teams and the use of automation to streamline the software development process.

    So, while the term “DevOps” is relatively new, the principles and practices that it encompasses have been in use for many years.

    In a traditional waterfall model, the software development process is split into distinct stages, with each stage being completed before moving on to the next one. The development and operations teams work in separate silos, with little interaction between them until the end of the development process when the finished product is handed over to the operations team for deployment and maintenance.

    Benefits of DevOps

    In contrast, a DevOps model emphasizes collaboration and communication between the development and operations teams throughout the entire software development life cycle. The goal is to create a culture of shared responsibility for the success of the project, with both teams working together to ensure that the software is developed, tested and deployed quickly and efficiently.

    DevOps also emphasizes automation, with the use of tools and processes to streamline the aforementioned project tasks. This automation helps to reduce the likelihood of errors and allows the teams to focus on higher-level tasks.

    Overall, the key differences between DevOps and a traditional waterfall model are the emphasis on collaboration and communication, the use of automation and the focus on shared responsibility for the success of the project.

    In short: Speed, time to market, agility and cost savings.

    What’s Driving DevOps Transformation?

    Any company, regardless of size, can release faster with DevOps. Look at the agile FinTech success stories; look at the generative AI success stories. In terms of cost savings too, it’s not ‘How much am I paying for 10 people, 20 people, 100 people…?’ From a DevOps standpoint, cost means penalties avoided from failing to release on time, project delays and project poor quality.

    Overall, development practices are evolving. Some clients call DevOps ‘zero rework’ development, others refer to it as ‘shift-left development.’ Whichever term you prefer, the context is the same: They want faster release and to reduce duplication of work. The way to achieve ‘shift-left’ or ‘zero rework’ would be through DevOps transformation. All of the individual pillars of DevOps concern shifting left, causing them to release faster and achieve their cost optimization.

    However you refer to it, DevOps is mainstream because of its scalability benefits, as well as the agility to react to unforeseen situations during development, QA or operations. This equates to competitive advantages and future-proofing.

    Challenges of Implementing DevOps

    Every business is different, but many of the challenges are the same. One of the main challenges is understanding your company’s specific blend of pain points on its road to DevOps transformation. Common challenges include:

    Vision – DevOps is not a top-down initiative. The role of executives in DevOps is to define a vision; if the vision is not clear, you won’t have buy-in. You can have the best tools and technology but if you don’t have buy-in, then it won’t work.

    Investment – DevOps is a change in every aspect of people, processes, technology and culture. You can’t just bring in this change with zero investment. It’s not unusual for costs to go up for the first couple of years after moving to DevOps because you’re transitioning from manual testers and normal developers to fungible team members that come at a premium. The cost advantage shows up after at least a year as rework goes down and the quality of release incrementally increases, leading to a better customer experience. So, it’s self-funded from the efficiencies it will bring in … but only if you persevere.

    Celebrating Success – Too many people try to boil the ocean with DevOps transformation. This leads to frustration, misaligned teams and, eventually, poor results. If you start small with DevOps transformation, it soon shows its superiority as a business model. This inspires your leadership and your executive teams to see the benefit of DevOps and buy into it by osmosis rather than taking the word of that executive with a big idea and a big purchase order.

    Culture and People – This is more than just a technology play. In fact, technology is the easy part. People, processes and culture all need to transform to implement DevOps. You need to move from a project to a product-based methodology. From a people perspective, you can’t bring in manual testers or manual validators to do DevOps. Any successful DevOps implementation has to get this right.

    How to Implement DevOps: The Hub and the Spoke

    Again, start small. You could start with only continuous integration and then move on to add continuous deployment. If you start over aspirationally, you are more likely to fail and go back to the old ways. As you expand DevOps gradually, you can follow the hub and spoke model.

    In the hub and spoke model, the hub is a lean team responsible for setting up the tools, automation and best practices. The hub is responsible for defining what is to be executed. The spoke, on the other hand, is responsible for execution. The spoke is the extended team and their individual projects which they are responsible for executing.

    The hub is also responsible for enablement by defining what processes, best practices, tools and pipelines should be used. Teams in the spoke will use the pipeline to promote their goal from development to QA, from QA to staging and from staging to production.

    Next-Gen DevOps

    The evolution of DevOps continues even for early adopters, especially with the introduction of AI and generative AI. How do you use AI to validate your requirements? How do you use AI to decide what infrastructure is needed and when? How can you use AI to optimize your test sets? Next-gen DevOps is about smarter DevOps and intelligent DevOps.

  • OpenAI to go Open Source — Elon Musk was a ‘Huge Idiot’ ¦ Mojo Risin’

    OpenAI to go Open Source — Elon Musk was a ‘Huge Idiot’ ¦ Mojo Risin’

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

    This week: Sources say it’s an open source future for OpenAI, and improving AI perf with Mojo. (more…)

  • GitHub Adds Accessibility Improvements for Developers With Disabilities

    GitHub Adds Accessibility Improvements for Developers With Disabilities

    GitHub today revealed it added several capabilities to make it easier for developers with disabilities to access its code repository platform. The new capabilities were announced to coincide with Global Accessibility Awareness Day.

    Capabilities highlighted include a navigation tool and a search tool that can be used with a screen reader that GitHub provides.

    In addition, GitHub’s contribution graph has been updated to remove barriers for keyboard-only and screen reader users.

    Finally, GitHub has made site-wide color contrast improvements to the light and dark themes presented by Primer, the GitHub Design System.

    Ed Summers, head of accessibility for GitHub, said there are more than 1.3 billion individuals with a wide variety of disabilities that could, with access to the right tools, write code. That community today, however, is underrepresented as a percentage of the larger IT community, he noted.

    However, as tools such as GitHub Copilot—a generative artificial intelligence (AI) tool that helps developers write better, more secure code—is paired with GitHub Copilot Voice—a speech interface for GitHub Copilot that is available in technical preview—the GitHub platform becomes more accessible to developers with disabilities, he said.

    Generative AI coupled with a speech interface that eliminates the need for a keyboard is lowering the barrier for entry for every potential developer. But for individuals with disabilities, it represents a tremendous advance, noted Summers. Anyone who understands how code should be constructed can build an application, he added.

    It’s not clear what impact generative AI will have on the global shortage of developer talent that has persisted for decades but it’s clear the pace at which applications can be developed is about to dramatically increase. In addition, many organizations have embraced working from home in the wake of the COVID-19 pandemic. That shift makes it easier for many individuals with disabilities to become part of the workforce because they no longer need to commute to an office.

    Generative AI platforms are fundamentally changing the way humans interact with machines. Instead of requiring a developer to create a level of abstraction to communicate with a machine, it’s now possible for machines to understand the language humans use to communicate with each other. Via a natural language interface, developers will soon be asking generative AI platforms to not only surface suggestions but also debug applications and write the code used to create an application.

    At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of DevOps teams, the ultimate impact should involve less drudgery as many of the manual tasks that conspire to make managing DevOps workflows tedious are eliminated.

    It may be a while before the impact of generative AI is fully appreciated, and there will certainly be more advances to come that will be applicable to individuals with disabilities. In the meantime, however, many members of a DevOps team may not even realize a member of the team has a disability as the writing of code continues to be increasingly automated.

  • Infrastructure-as-Code Pitfalls in Platform Engineering

    Infrastructure-as-Code Pitfalls in Platform Engineering

    In the context of cloud operations, the last decade was ruled by DevOps and infrastructure-as-code (IaC). But what is true DevOps? Is it developers running their own operations? Is it operators learning development skills to automate infrastructure provisioning using infrastructure-as-code (IaC) tools like Terraform?

    Though DevOps is widely understood to incorporate both of these, software developers are far from fully understanding or operating infrastructure. IaC is purely a DevOps tool. When an organization claims to have automated its infrastructure, they mean automation for the efficiency of the DevOps teams, not automation for developers. Developers are still left waiting on DevOps for days for even small infrastructure changes.

    While there were efficiencies gained by the current DevOps model, there is an increasing acknowledgment that we require a better approach to enable developer self-service. This has led to the platform engineering discipline.

    The high-level goals of platform engineering are:

    • Developer self-service for significant parts of infrastructure updates without DevOps subject matter expertise.
    • Built-in security and compliance controls.

    Infrastructure-as-Code (IaC) Limitations in Platform Engineering

    A DuploCloud survey of 40+ enterprises that have made substantial investments in platform engineering showed that the prevalent approach is for DevOps teams to build a DevOps platform with IaC as a core underlying technology. They create templates for the organization’s use cases and publish them in a CI/CD pipeline or a self-service catalog.

    Here are the top reasons why platforms that build on top of IaC are failing these platform engineering goals:

    1. IaC templates are rigid and not flexible enough to meet changing developer requirements: It is true that DevOps teams can anticipate cloud infrastructure topologies to some extent and have IaC templates for those with a few customizable parameters. But in a microservices world, there are thousands of other workflows and topologies possible based on changing application needs and security controls. Large data centers and edge infrastructure make this even more complex. A manual approach, relying solely on DevOps personnel to constantly build and update myriad combinations in static scripts simply can’t scale.
    2. Scripting tools can’t build life cycle management: In cloud operations, people-triggered changes are only a subset of possible use cases. Many asynchronous operations need to be continuously performed. These range from detection of configurations, from desired state to reverting, or complex configuration scenarios where individual components have to be set up asynchronously and brought together later. It could be as simple as a certain component going down and needing to be restored. IaC is a script that runs when triggered to completion. It has no active life cycle to operate continuously in the background.
    3. Inability to build a concept of an environment: In any orchestration system, users have a concept of the environment they want to build. When they log in to the platform and navigate to their environment, they expect to update as well as view the state and aspects of resources in that environment, be it the provisioning status, metrics, logs, faults, audit logs, compliance posture, etc. They may want to perform debugging functions, such as restarting services, SSHinginto a VM or accessing a resource’s cloud console (S3 Console, for example) using access control boundaries within that environment. For example, in Kubernetes, you choose the namespace to be the environment and management software like Rancher on top of K8S to provide these functions. If we must replicate this same concept across a broad infrastructure-level platform, we cannot do it using only IaC. We need something with a scope that spans all cloud operations, like Kubernetes. Terraform is a configuration updating and management software, not an orchestration system.

    Many platform teams have tried to work around these problems with point solutions by using a disparate set of jobs for certain aspects of life cycle management, building a thin UI shim on top of cloud accounts for visualization of resources while redirecting to other systems like Datadog for logging, metrics and alerts. But for most of the use cases, the DevOps team is still very much in the operations workflow. This completely defeats the concept of developer self-service as well as continuous compliance goals.

    Learning From Other Successful Platforms

    Two recent examples of successful cloud platforms are Amazon Web Services (AWS) in the context of infrastructure-as-a-service (IaaS) and Kubernetes for container orchestration. These are distributed system implementations using higher-level programming languages like Java and Go. You can’t build such complex systems using scripts and jobs.

    Building a true DevOps orchestration platform requires a systems design approach. It also takes expert systems engineers and many years to build and mature, like it did for K8s and IaaS in the Public Cloud.

    Conclusion

    Since platform engineering teams need systems design expertise that is likely outside of the scope of the typical DevOps role, we need the software development function to intervene with distributed systems programmers and many years of investment. Most organizations trying to build in-house platforms don’t have or don’t want to make this type of investment, as it may not be their core business. Megascale tech companies like Facebook, Uber, Netflix, etc. will likely build an in-house solution, as they have the talent and scale, but for the masses, it is likely that the solution will be an off-the-shelf product from an ISV.

    The industry has solved IaaS with public cloud, container orchestration with Kubernetes and observability with platforms like Datadog. The platform engineering category is heading in the same direction.

  • What’s the Difference Between DevOps and Platform Engineering?

    What’s the Difference Between DevOps and Platform Engineering?

    The DevOps space is buzzing with a new trend—platform engineering. Platform engineering has been gaining steam over the last few years but has really peaked in interest lately. In fact, according to a study conducted by Puppet Labs, 51% of organizations have adopted platform engineering or plan to do so in the next year. But what exactly is platform engineering, and how is it different than DevOps?

    Below, we’ll explore the nuances between these two concepts. We’ll review what DevOps and platform engineering are and see how they’re similar and dissimilar. We’ll also outline the benefits of platform engineering and suggest some best practices to make the most of this emerging discipline.

    What is DevOps?

    DevOps is a broad topic, but it generally involves efforts to increase software release efficiency and frequency. Some of the hallmarks of the DevOps discipline include reducing siloes between teams and increasing collaboration. DevOps is often directly associated with various tools that automate the process of moving code from a local environment into production, also known as continuous integration and continuous delivery (CI/CD). Monitoring, incident management and configuration management tools also support the DevOps process.

    “DevOps reflects more of a philosophy and a way of working,” said David Sandilands, senior solutions architect for Puppet. DevOps enables more of a shift left mentality. Instead of having operations teams “guarding the gates,” developers work in partnership with Ops. In doing so, groups can decrease build time significantly, he said. This style helps engineers incrementally and rapidly release new features, replacing old waterfall development cycles.

    The ultimate goal of DevOps is to automate the software life cycle and shorten the feedback loop. Thus, DevOps takes a life cycle approach to shipping applications more efficiently, streamlining phases such as build, release, configure, monitor, plan, create and verify. Microservices architecture and containerization also often play a crucial role in enabling DevOps.

    What is Platform Engineering?

    Platform engineering is all about developing a self-service internal platform that serves the needs of developer consumers. Although DevOps opened things up and empowered individual developers with advanced automation, it left organizations with scattered tooling and processes, especially at large enterprises. For example, various infrastructure-as-code (IaC) configurations might exist for similar functions within the same organization but be unknown to engineers in different departments. Not to mention, overseeing a growing DevOps pipeline means added responsibilities around continual updates and security.

    Platform engineering places more control in the hands of a central platform team rather than individual developers. These teams typically treat the platform as a product. They do so by evaluating the needs of developers across an organization, building out internal self-service tools that suit the needs of most consumers and maintaining the platform.

    The 2023 State of DevOps Report found that this self-service approach typically handles areas like deployment, operation of infrastructure, monitoring production applications and security and compliance. Other capabilities, such as abstracting Kubernetes or overseeing secrets management, may also play a role in these efforts.

    Benefits of Platform Engineering

    • Improve developer experience: Platform engineering can enhance the experience a developer has with their software build and release systems. A better developer experience can do loads to improve morale and create a more productive engineering atmosphere.
    • Standardize DevOps practices: By bringing together common DevOps approaches into a shared platform, organizations can begin to standardize with reusable build processes and automated infrastructure. This can unite an organization on common runtimes, observability methods and deployment processes. This can streamline the introduction of DevOps practices to an organization.
    • Secure the DevOps pipeline: Various vulnerabilities exist within tooling that comprises modern CI/CD chains and cloud-native open source software. By outsourcing DevOps responsibility to a shared platform team, the organization can coalesce around more secure practices and avoid overlooking important updates or holes.
    • Supply additional guardrails: Platform teams might also assist in the acquisition of new tools as well as outlining and enforcing policies for software infrastructure. While this may come at the cost of customization, additional guardrails can help avoid shadow IT and governance around internal development. Letting platform teams handle it could make maintenance easier across an organization.

    Platform Engineering: Under the DevOps Umbrella

    I don’t see platform engineering as replacing DevOps. Instead, I view platform engineering as falling under the umbrella of DevOps since platform engineering takes DevOps processes and makes them more reusable and self-service. Both use a lot of automation and share many of the same end goals.

    However, there are nuances between the two approaches that reflect the recent trends in the IT market. With DevOps, the market was more interested in shifting responsibility left and empowering individual developers to oversee the entire software life cycle. Now, as sprawl and shadow IT emerge, the pendulum seems to be shifting back toward a bit more centralization and governance with platform engineering.

    Platform engineering is typically more of an in-house development initiative. So, what are some best practices IT divisions should remember to get the most out of platform engineering? Hope Lynch, senior director of platform at Cloudbees, shared some great strategies to consider that revolve around treating the platform as a product. Doing this involves accepting feedback from developers, not controlling everything and always maintaining a focus on developer experience.

    Hopefully, by following these tips, organizations should have a better grasp of the emerging discipline of platform engineering and have the confidence to begin introducing platform-based concepts within their own environments.

  • Nobl9 Adds Tools to Make SLO Queries More Efficient

    Nobl9 Adds Tools to Make SLO Queries More Efficient

    At its SLOconf event today, Nobl9 added a Query Tester tool that validates whether queries made against the data its service level objectives (SLO) management platform collects will deliver meaningful results. Nobl9’s SLO management platform collects data in real-time from data sources such as New Relic, Datadog and Dynatrace to help customers meet SLOs.

    In addition, a Query Checker tool will now automatically put any new SLO into a testing state to automatically validate that the query against that SLO will run correctly.

    The company has also improved the precision of its calculations to provide DevOps teams with more accurate, reliable and actionable insights into the performance of services and added a Metrics Health Notifier toolset that provides insights into anomalous data being collected from various sources as well as what data might be missing. Nobl9 has also added a Custom Query Delay tool that gives DevOps teams more control over when data is pulled along with access to data source logs.

    Kit Merker, chief growth officer for Nobl9, said the overall goal is to improve the overall resiliency of its platform for managing SLOs.

    Finally, Nobl9 also announced that its platform is now available for the Google Cloud Platform (GCP) in addition to existing support for Amazon Web Services (AWS). As part of that GCP support, Nobl9 made available an educational site that provides example of how DevOps team can take advantage of the large language models (LLMs) that Google is making to improve SLOs or generate an SLO that is automatically converted into a set of YAML files.

    A survey of 314 IT professionals conducted by Dimensional Insight on behalf of Nobl9 found 69% have implemented SLOs, with more than three-quarters of those respondents crediting SLOs for preventing business disruptions. A total of 80% also noted their organization has an increased focus on system reliability, with just over a quarter (27%) saying SLOs saved their organizations a half million dollars or more. A full 95% said SLOs are also driving better business decisions.

    However, 97% also reported it is difficult to manage SLOs because of a lack of reporting, lack of application and environment support and interoperability issues.

    Nobl9 is trying to spur greater adoption of SLOs by making available an open source SLO specification that defines a common interface for constructing SLOs across a Git-based workflow. Nobl9’s platform based on that specification has now been integrated with 24 data sources, said Merker.

    SLOs, of course, are not a new idea. They have been employed as a metric to track the performance of IT services for decades. However, as more microservices-based applications are built and deployed, it’s becoming more challenging to maintain SLOs across applications that have many more dependencies than legacy monolithic applications.

    Ultimately, each IT team needs to provide some sort of objective benchmark that assesses their overall effectiveness at delivering application services. SLOs-as-code are intended to make it simpler to gather the metrics that confirm whether service levels are being achieved. Those SLOs are then tracked and integrated across any set of application services a DevOps team cares to track.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

    Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

    Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

    The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

    We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

    Dice.com

    ERP International
    Fort Meade, Maryland
    Senior DevOps Infrastructure Engineer
    $150,000 to $180,000

    LinkedIn

    Lark Health
    Remote
    Lead DevOps Engineer
    $127,700 to $183,900

    Simply Hired

    Shake Shack
    Remote
    Senior DevOps Engineer
    $123,000 to $155,000

    Indeed.com

    Peraton
    Arlington, Virginia
    DevSecOps Data Automation Engineer
    $112,000 to $179,000

    CareerBuilder.com

    Booz Allen Hamilton
    Fort Meade, Maryland
    DevOps Engineer and Release Manager
    $73,100 to $166,000

  • Google Previews Generative AI Service to Improve Code

    Google Previews Generative AI Service to Improve Code

    Google is previewing an instance of a generative artificial intelligence (AI) service that surfaces code recommendations to make it possible to build high-quality applications faster.

    Specifically, Duet AI for Google Cloud surfaces code recommendations in real-time, generates full functions and code blocks and suggests fixes for vulnerabilities and errors in the code that it discovers. It also promises to enable DevOps teams to create custom large language models (LLM) based on their own code. In fact, GitLab and Tabnine are, respectively, already integrating LLMs running on Google Cloud with their continuous integration/continuous delivery (CI/CD) platform and testing tools.

    Chen Goldberg, vice president of engineering for Google, told attendees at the Google I/O 2023 conference that Duet AI for Google Cloud will fundamentally change how developers will be able to build applications, regardless of their skill level.

    Duet AI for Google Cloud was trained using data collected from Google Cloud using sample code and documentation to fine-tune behaviors and patterns.

    Duet AI for Google Cloud is available under an AI Trusted Tester Program and provides developers with many of the same capabilities that can be found in the GitHub Copilot tool that is based on the LLMs created by OpenAI.

    In addition to making Duet AI for Google Cloud accessible via both its tools and managed services, Google said it also plans to integrate the service with multiple integrated development environments (IDEs).

    It’s still early days as far as the use of generative AI within DevOps workflows is concerned, but it’s clear that the amount of code being created by developers is about to increase exponentially. Hopefully, AI technologies will also one day help software engineers find ways to manage the massive volume of code moving across their DevOps pipelines.

    Generative AI platforms will fundamentally change the way humans interact with machines. Instead of requiring a developer to create a level of abstraction to communicate with a machine, it’s now possible for machines to understand the language humans use to communicate with each other. Using a natural language interface, developers will soon be asking generative AI platforms to not only make suggestions but also debug applications and, eventually, write the code used to create an application.

    At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of DevOps teams, the ultimate impact should involve less drudgery and toil as many of the manual tasks that conspire to make managing DevOps workflows tedious are eliminated.

    In the meantime, DevOps teams should spend some time identifying which tasks will become automated by generative AI and other technologies that employ machine and deep learning algorithms. It doesn’t make much sense to onboard new members to a DevOps team only to discover that the tasks assigned to that individual are about to be automated. Hopefully, when all these AI technologies are finally implemented, the entire DevOps experience will be better than ever.