[{"data":1,"prerenderedAt":818},["ShallowReactive",2],{"/en-us/blog/there-is-no-mlops-without-devsecops":3,"navigation-en-us":41,"banner-en-us":451,"footer-en-us":461,"blog-post-authors-en-us-William Arias":700,"blog-related-posts-en-us-there-is-no-mlops-without-devsecops":714,"blog-promotions-en-us":756,"next-steps-en-us":808},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":40},"blogPosts/en-us/blog/there-is-no-mlops-without-devsecops.yml","There Is No Mlops Without Devsecops",[7],"william-arias",null,"ai-ml",{"slug":11,"featured":12,"template":13},"there-is-no-mlops-without-devsecops",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Building GitLab with GitLab: Why there is no MLOps without DevSecOps","Follow along as data scientists adopt DevSecOps practices and enjoy the benefits of automation, repeatable workflows, standardization, and automatic provisioning of infrastructure.",[18],"William Arias","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659740/Blog/Hero%20Images/building-gitlab-with-gitlab-no-type.png","2023-10-05","Building predictive models requires a good amount of experimentation and iterations. Data scientists building those models usually implement workflows involving several steps such as data loading, processing, training, testing, and deployment. Such workflows or data science pipelines come with a set of challenges on their own; some of these common challenges are:\n\n- prone to error due to manual steps\n\n- experimentation results that are hard to replicate\n\n- long training time of machine learning (ML) models\n\nWhen there is a challenge, there is also an opportunity; in this case, those challenges represent an opportunity for data scientists to adopt DevSecOps practices and enjoy the benefits of automation, repeatable workflows, standardization, and automatic provisioning of infrastructure needed for data-driven applications at scale.\n\nThe [Data Science team at\nGitLab](https://handbook.gitlab.com/handbook/enterprise-data/organization/data-science/)\nis now utilizing the GitLab DevSecOps Platform in their workflows, specifically to:\n\n- enhance experiment reproducibility by ensuring code and data execute in a\nstandardized container image\n\n- automate training and re-training of ML models with GPU-enabled CI/CD\n\n- leverage ML experiment tracking, storing the most relevant metadata and\nartifacts produced by data science pipelines automated with CI\n\nAt GitLab, we are proponents of \"dogfooding\" our platform and sharing how we use GitLab to build GitLab. What follows is a detailed look at the Data\nScience team's experience.\n\n### Enhancing experiment reproducibility\n\nA baseline step to enhance reproducibility is having a common and standard experiment environment for all data scientists to run experiments in their\nJupyter Notebooks. A standard data science environment ensures that all team members use the same software dependencies. A way to achieve this is by building a container image with all the respective dependencies under version control and re-pulling it every time a new version of the code is run. This process is illustrated in the figure below:\n\n![build](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/build-2.png)\n\nData science image of automatic build using GitLab CI\n\nYou might wonder if the image gets built every time there is a new commit.\nThe answer is \"no\" since that would result in longer execution times, and the image dependencies versions don’t change frequently, rendering it unnecessary to build it every time there is a new commit. Therefore, once the standard image is automatically built by the pipeline, it is pushed to the GitLab Container Registry, where it is stored and ready to be pulled every time changes to the model code are introduced, and re-training is necessary.\n\n![registry](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/registry.png)\n\nGitLab Container Registry with image automatically built and pushed by a CI pipeline\n\nChanges to the image dependencies or Dockerfile require a [merge request](https://docs.gitlab.com/ee/user/project/merge_requests/) and an approval process.\n\n### How to build the data science image using GitLab CI/CD\n\nConsider this project structure:\n\n```text\nnotebooks/\n.gitlab-ci.yml\nDockerfile\nconfig.yml\nrequirements.txt\n```\n\nGitLab's Data Science team already had a pre-configured JupyterLab image with packages such as [gitlabds](https://pypi.org/project/gitlabds/1.0.0/)\nfor common data preparation tasks and modules to enable Snowflake connectivity for loading raw data. All these dependencies are reflected in the Dockerfile at the root of the project, plus all the steps necessary to build the image:\n\n```text\nFROM nvcr.io/nvidia/cuda:12.1.1-base-ubuntu22.04\nCOPY .    /app/\nWORKDIR /app\nRUN apt-get update\nRUN apt-get install -y python3.9\nRUN apt-get install -y python3-pip\nRUN pip install -r requirements.txt\n```\n\nThe instructions to build the data science image start with using Ubuntu with CUDA drivers as a base image. We are using this baseline image because, moving forward, we will use GPU hardware to train models. The rest of the steps include installing Python 3.9 and the dependencies listed in `requirements.txt` with their respective versions.\n\nAutomatically building the data science image using [GitLab\nCI/CD](https://about.gitlab.com/topics/ci-cd/) requires us to create the `.gitlab-ci.yml ` at the root of the project and use it to describe the jobs we want to automate. For the time being, let’s focus only on the `build-ds-image`job:\n\n```yaml\n\nvariables:\n  DOCKER_HOST: tcp://docker:2375\n  MOUNT_POINT: \"/builds/$CI_PROJECT_PATH/mnt\"\n  CONTAINER_IMAGE: \"$CI_REGISTRY_IMAGE/main-image:latest\"\n\nstages:\n    - build\n    - train\n    - notify\ninclude:\n  - template: 'Workflows/MergeRequest-Pipelines.gitlab-ci.yml'\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n\nbuild-ds-image:\n  tags: [ saas-linux-large-amd64 ]\n  stage: build\n  services:\n    - docker:20.10.16-dind\n  image:\n    name: docker:20.10.16\n  script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - docker build -t $CONTAINER_IMAGE .\n    - docker push $CONTAINER_IMAGE\n  rules:\n    - if: '$CI_PIPELINE_SOURCE == \"merge_request_event\" && $CI_MERGE_REQUEST_TARGET_BRANCH_NAME == $CI_DEFAULT_BRANCH'\n      changes:\n        - Dockerfile\n        - requirements.txt\n\n  allow_failure: true\n```\n\nAt a high level, the job `build-ds-image`:\n\n- uses a docker-in-docker service (dind) necessary to create docker images\nin GitLab CI/CD.\n\n- uses predefined variables to log into the GitLab Container\nRegistry, build the image, tag it using $CONTAINER_IMAGE variable, and push it to the registry. These steps are declared in the script section lines.\n\n- leverages a  `rules` section to evaluate conditions to determine if the\njob should be created. In this case, this job runs only if there are changes to the Dockerfile and requirements.txt file and if those changes are created using a merge request.\n\nThe conditions declared in `rules` helps us optimize the pipeline running time since the image gets rebuilt only when necessary.\n\nA complete pipeline can be found in this example project, along with instructions to trigger the automatic creation of the data science image:\n[Data Science CI pipeline](https://gitlab.com/gitlab-data/data-science-ci-example/-/blob/main/.gitlab-ci.yml?ref_type=heads).\n\n### Automate training and re-training of ML models with GPU-enabled CI/CD\n\nGitLab offers the ability to leverage GPU hardware and, even better, to get this hardware automatically provisioned to run jobs declared in the\n.gitlab-ci.yml file. We took advantage of this capability to train our ML models faster without spending time setting up or configuring graphics card drivers. Using GPU hardware ([GitLab\nRunners](https://docs.gitlab.com/ee/ci/runners/saas/gpu_saas_runner.html))\nrequires us to add this line to the training job:\n\n```yaml\n\ntags:\n        - saas-linux-medium-amd64-gpu-standard\n```\n\nThe tag above will ensure that a GPU GitLab Runner automatically picks up every training job.\n\nLet’s take a look at the entire training job in the .gitlab-ci.yml file and break down what it does:\n\n```text\n\ntrain-commit-activated:\n    stage: train\n    image: $CONTAINER_IMAGE\n    tags:\n        - saas-linux-medium-amd64-gpu-standard\n    script:\n        - echo \"GPU training activated by commit message\"\n        - echo \"message passed is $CI_COMMIT_MESSAGE\"\n        - notebookName=$(echo ${CI_COMMIT_MESSAGE/train})\n        - echo \"Notebook name $notebookName\"\n        - papermill -p is_local_development False -p tree_method 'gpu_hist' $notebookName -\n    rules:\n        - if: '$CI_COMMIT_BRANCH == \"staging\"'\n          when: never\n        - if: $CI_COMMIT_MESSAGE =~ /\\w+\\.ipynb/\n          when: always\n          allow_failure: true\n    artifacts:\n      paths:\n        - ./model_metrics.md\n````\n\nLet’s start with this block:\n\n```yaml\n\ntrain-commit-activated:\n    stage: train\n    image: $CONTAINER_IMAGE\n    tags:\n        - saas-linux-medium-amd64-gpu-standard\n```\n\n- **train-commit-activated** This is the name of the job. Since the model\ntraining gets activated given a specific pattern in the commit message, we use a descriptive name to easily identify it in the larger pipeline.\n\n- **stage: train** This specifies the pipeline stage where this job belongs.\nIn the first part of the CI/CD configuration, we defined three stages for this pipeline: `build`, `train`,  and `notify`. This job comes after building the data science container image. The order is essential since we first need the image built to run our training code in it.\n\n- **image: $CONTAINER_IMAGE** Here, we specify the Docker image built in the\nfirst job that contains the CUDA drivers and necessary Python dependencies to run this job. $CONTAINER_IMAGE is a user-defined variable specified in the variables section of the .gitlab-ci.yml file.\n\n- **tags: saas-linux-medium-amd64-gpu-standard** As mentioned earlier, using\nthis line, we ask GitLab to automatically provision a GPU-enabled Runner to execute this job.\n\nThe second block of the job:\n\n```markdown\nscript:\n        - echo \"GPU training activated by commit message\"\n        - echo \"message passed is $CI_COMMIT_MESSAGE\"\n        - notebookName=$(echo ${CI_COMMIT_MESSAGE/train})\n        - echo \"Notebook name $notebookName\"\n        - papermill -p is_local_development False -p tree_method 'gpu_hist' $notebookName -\n```\n\n- **script** This section contains the commands in charge of running the\nmodel training. The execution of this job is conditioned to the contents of the  commit message. The commit message must have the name of the Jupyter\nNotebook that contains the actual model training code.\n\nThe rationale behind this approach is that we wanted to keep the data scientist workflow as simple as possible. The team had already adopted the [modeling templates](https://gitlab.com/gitlab-data/data-science/-/tree/main/templates)\nto start building predictive models quickly. Plugging the CI pipeline into their modeling workflow was a priority to ensure productivity would remain intact. With these steps:\n\n```text\nnotebookName=$(echo ${CI_COMMIT_MESSAGE/train})\n        - echo \"Notebook name $notebookName\"\n        - papermill -p is_local_development False -p tree_method 'gpu_hist' $notebookName -\n```\n\nThe CI pipeline captures the name of the Jupyter Notebook with the training modeling template and passes parameters to ensure [XGBoost](https://xgboost.readthedocs.io/en/stable/) uses the provisioned\nGPU. You can find an example of the Jupyter modeling template that is executed in this job [here](https://gitlab.com/gitlab-data/data-science-ci-example/-/blob/main/notebooks/training_example.ipynb?ref_type=heads).\n\nOnce the data science image is built, it can be reutilized in further model training jobs. The `train-commit-activated` job pulls the image from the\nGitLab Container Registry and utilizes it to run the ML pipeline defined in the training notebook. This is illustrated in the `CI Job - Train model` in the figure below:\n\n![training](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/training_job.png)\n\nTraining job executes ML pipeline defined in the modeling notebook\n\nSince our image contains CUDA drivers and GitLab automatically provisions\nGPU-enabled hardware, the training job runs significantly faster with respect to standard hardware.\n\n### Using GitLab ML experiment tracker\n\nEach model training execution triggered using GitLab CI is an experiment that needs tracking. Using Experiment tracking in GitLab helps us to record metadata that comes in handy to compare model performance and collaborate with other data scientists by making result experiments available for everyone and providing a detailed history of the model development.\n\n![experiments](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/experiments.png)\n\nExperiments automatically logged on every CI pipeline GPU training run\n\nEach model artifact created can be traced back to the pipeline that generated it, along with its dependencies:\n\n![traceability](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/traceability_small.png)\n\nModel traceability from pipeline run to candidate details\n### Putting it all together\n\nWhat is machine learning without data to learn from? We also leveraged the [Snowflake](https://www.snowflake.com/en/) connector in the model training notebook and automated the data extraction whenever the respective commit triggers a training job. Here is an architecture of the current solution with all the parts described in this blog post:\n\n![process](https://about.gitlab.com/images/blogimages/2023-10-04-there-is-no-mlops-without-devsecops/training_fixed.png)\n\nData Science pipelines automated using GitLab DevSecops Platform\n| Challenge | Solution |\n| --- | --- |\n| Prone to error due to manual steps | Automate steps with [GitLab CI/CD](https://docs.gitlab.com/ee/ci/) |\n| Experimentation results that are hard to replicate | Record metadata and model artifacts with [GitLab Experiment Tracker](https://docs.gitlab.com/ee/user/project/ml/experiment_tracking/) |\n| The long training time of machine learning models | Train models with [GitLab SaaS GPU Runners](https://docs.gitlab.com/ee/ci/runners/saas/gpu_saas_runner.html) |\n\nIterating on these challenges is a first step towards MLOps, and we are at the tip of the iceberg; in coming iterations, we will adopt security features to ensure model provenance (software bill of materials) and code quality, and to monitor our ML workflow development with value stream dashboards. But so far, one thing is sure: **There is no MLOps without\nDevSecOps**.\n\nGet started automating your data science pipelines, follow this [tutorial](https://handbook.gitlab.com/handbook/enterprise-data/platform/ci-for-ds-pipelines/)\nand clone this [data-science-project](https://gitlab.com/gitlab-data/data-science-ci-example)\nto follow along and watch this demo of using GPU Runners to train [XGBoost](https://xgboost.readthedocs.io/en/stable/) model.\n\nSee how data scientists can train ML models with GitLab GPU-enabled Runners (XGBoost 5-minute demo):\n\n\u003C!-- blank line -->\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/tElegG4NCZ0?si=L1IZfx_UGv6u81Gk\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\n\u003C!-- blank line -->\n\n## More \"Building GitLab with GitLab\" blogs\n\nRead more of our \"Building GitLab with GitLab\" series:\n\n- [How we use Web API fuzz\ntesting](https://about.gitlab.com/blog/building-gitlab-with-gitlab-api-fuzzing-workflow/)\n\n- [How GitLab.com inspired GitLab\nDedicated](https://about.gitlab.com/blog/building-gitlab-with-gitlabcom-how-gitlab-inspired-dedicated/)",[23,24,25,26],"tutorial","DevSecOps","DevSecOps platform","AI/ML","yml",{},true,"/en-us/blog/there-is-no-mlops-without-devsecops",{"ogTitle":15,"ogImage":19,"ogDescription":16,"ogSiteName":32,"noIndex":12,"ogType":33,"ogUrl":34,"title":15,"canonicalUrls":34,"description":16},"https://about.gitlab.com","article","https://about.gitlab.com/blog/there-is-no-mlops-without-devsecops","en-us/blog/there-is-no-mlops-without-devsecops",[23,37,38,39],"devsecops","devsecops-platform","aiml","Ck0fQPtjQbE5ALZBEluYc8bq_U9WH6LC1Dz7yDRy1tI",{"data":42},{"logo":43,"freeTrial":48,"sales":53,"login":58,"items":63,"search":371,"minimal":402,"duo":421,"switchNav":430,"pricingDeployment":441},{"config":44},{"href":45,"dataGaName":46,"dataGaLocation":47},"/","gitlab 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and Vertex AI on Google Cloud: Advancing agentic software development","Learn how Google Cloud customers are standardizing on GitLab and Vertex AI for foundation models, enterprise controls, and Model Garden breadth.\n",[720,721],"Regnard Raquedan","Rajesh Agadi","GitLab Duo Agent Platform is helping redefine how organizations build, secure, and deliver software. Since its general availability in January 2026, the platform is bringing agentic AI to every phase of the software development lifecycle. Duo Agent Platform is an intelligent orchestration layer where software teams, and their specialized agents plan, code, review, and remediate security vulnerabilities together.\n\nThrough this exciting partnership, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) automates software development orchestration and lifecycle context via its integration with Vertex AI on Google Cloud, which powers the model tier for agent calls. Software teams keep working on issues, merge requests, pipelines, and security workflows while inference follows the Google Cloud posture they already defined. \n\nAdvances in Google Cloud’s Vertex AI models expand how Google Cloud customers can use GitLab Duo Agent Platform in their environment. Customers get an AI-powered DevSecOps control plane in GitLab, backed by a rapidly advancing AI infrastructure foundation in Vertex AI and Duo Agent Platform’s flexible deployment and integration options. The combination enables more capable, governed agentic workflows that operate at enterprise scale.\n\n![Conceptual illustration of the GitLab Duo Agent Platform integrated with Google Cloud's Vertex AI to power agentic software development and governed AI workflows](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776165990/b7jlux9kydafncwy8spc.png)\n\n## Agents that work across the full lifecycle\n\nMany AI tools focus on a single task: generating code faster. GitLab Duo Agent Platform goes further. It orchestrates AI agents across the entire software development lifecycle (SDLC), from planning through security review to delivery, across many teams with many projects and releases. At this scale, AI coding assistants are necessary for continuous innovation but not sufficient. \n\nSingle-purpose coding assistants rarely see the full state of a project. Backlog shape, open merge requests, failing jobs, and security findings live in GitLab, but a separate chat window in a coding assistant does not inherit that full picture of the SDLC. The gap shows up as manual handoffs, duplicate explanations to an AI that lacks context, and governance teams trying to map data flows across tools that were never designed as one system.\n\nGitLab Duo Agent Platform helps close that gap by running agents and flows on the same objects engineers use every day. Vertex AI then supplies the models and services those agents call when Google Cloud is your chosen inference home, with GitLab’s AI Gateway mediating access so administrators keep a clear map of what connects to what. For instance, GitLab Duo Planner Agent analyzes backlogs, breaks epics into structured tasks, and applies prioritization frameworks to help teams decide what to build next. Security Analyst Agent triages vulnerabilities, details risks in plain language, and recommends remediation in priority order. Built-in flows connect these agents into end-to-end processes, without requiring developers to manage every handoff manually.\n\nAgentic Chat in GitLab Duo Agent Platform ties the experience together for developers. They query in natural language to get context-aware responses with multi-step reasoning that draws on the full state of a project: its issues, merge requests, pipelines, security findings, and codebase. Because GitLab serves as the system of record for the SDLC with a unified data model, GitLab Duo agents operate with lifecycle context that falls outside the reach of standalone, tool-specific AI assistants.\n\n### Amplified by Vertex AI\n\nGitLab Duo Agent Platform is designed to be model-flexible, routing different capabilities to different models based on what performs best for a given task. That architectural choice pays off on Google Cloud, where Vertex AI acts as the managed environment for foundation models and related services, providing a broad model ecosystem and managed infrastructure that helps push the platform's capabilities further.\n\nThe latest generations of AI models available through Vertex AI bring significant improvements in reasoning, tool use, and long-context understanding compared to previous iterations — the same properties that GitLab's agents rely on across many projects and teams with large, complex codebases. Longer context windows and richer tool integration in the underlying models expand what agents can accomplish in a single pass, which is especially important for workloads like deep backlog analysis or monorepo security review.\n\n[Vertex AI Model Garden](https://cloud.google.com/model-garden), with access to a wide range of foundation models, gives customers the breadth to make these choices based on performance, cost, and regulatory requirements rather than vendor lock-in.\n\nMoreover, GitLab customers can use Bring Your Own Model (BYOM) for Duo Agent Platform so approved providers and gateways land where your security model expects them. GitLab’s [18.9 launch coverage of self-hosted Duo Agent Platform and BYOM](https://about.gitlab.com/blog/agentic-ai-enterprise-control-self-hosted-duo-agent-platform-and-byom/) describes how that wiring works. With this deployment option, customers gain access to a wider set of model options they can tailor to their software development process: the right model for the right workflow, with the right guardrails.\n\nFor GitLab, the decision to build on Vertex AI was driven by the need for enterprise-grade reliability and unparalleled model breadth. Vertex AI and Model Garden completely abstract the heavy lifting of LLM hosting — meaning rapid version delivery, robust security, and strict governance are seamlessly built into the integration. Beyond offering Gemini models, Vertex AI provides global, low-latency access to a vast catalog of third-party and open-source models. \n\nCombined with Google Cloud's industry-leading approach to data privacy and model protection, Vertex AI emerged as the clear choice to power GitLab's next-generation developer experience. \n\nBy integrating Vertex AI Model Garden into its backend, GitLab supercharges its DevSecOps platform without passing any complexity on to users. Development teams are not burdened with evaluating or managing underlying LLMs; instead, they experience a streamlined, AI-assisted workflow for building their applications. \n\nGitLab completely abstracts cloud orchestration, enabling developers to focus entirely on writing great code, while Vertex AI powers the features and functionality that assist them.\n\n## What this means for customers on Google Cloud\n\nGitLab Duo Agent Platform already delivers AI agents that operate across the full software lifecycle within a single, governed system of record. On Google Cloud, it enables rapid innovation as Vertex AI continues to advance the model and infrastructure layers. \n\nFor Google Cloud customers, this integration means streamlined software delivery while maintaining strict enterprise governance. For platform engineering groups, it means normalizing which Vertex-backed models power suggestions, analysis, and remediation inside GitLab instead of cataloging dozens of client-side tools. Security programs benefit when agents propose and validate fixes in the same place developers already triage findings, cutting context switching and reducing work that would otherwise spill into unmanaged channels.\n\nFrom a cloud economics and policy angle, drawing agent inference toward Vertex from within GitLab keeps usage nearer to the agreements and controls you already run on Google Cloud, which helps avoid duplicate spend and shadow paths that bypass procurement.\n\nBecause Vertex AI is an underlying infrastructure provider for GitLab Duo Agent Platform, organizations are enabled to dramatically lift developer productivity without the overhead and risk of managing fragmented AI toolchains. Teams stay aligned within a single, secure system of record, helping them build applications faster and ship with confidence.\n\nThe GitLab and Google Cloud collaboration has been building since 2018. Today, it represents one of the most comprehensive paths for organizations moving from AI experiments to fully governed, agentic software development on Google Cloud. As both platforms continue to advance — GitLab expanding its agent orchestration and developer context, and Vertex AI pushing the boundaries of model capability and agent infrastructure — the value for joint customers will continue to grow.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/free-trial/) to experience the power of GitLab and Vertex AI on Google Cloud.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663121/Blog/Hero%20Images/LogoLockupPlusLight.png","2026-04-14",[26,278,726,727,728],"google","news","product",{"featured":29,"template":13,"slug":730},"gitlab-and-vertex-ai-on-google-cloud",{"content":732,"config":741},{"heroImage":733,"title":734,"description":735,"authors":736,"date":738,"category":9,"tags":739,"body":740},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643639/sapu29gmlgtwvhggmj6k.png","Extend GitLab Duo Agent Platform: Connect any tool with MCP","Learn how to connect external tools to GitLab Duo Agent Platform using MCP. Step-by-step setup with three practical workflow demos.",[737],"Albert Rabassa","2026-03-05",[9,728,23],"Managing software development often means juggling multiple tools: tracking issues in Jira, writing code in your IDE, and collaborating through GitLab. Context switching between these platforms disrupts focus and slows down delivery.\n\nWith GitLab Duo Agent Platform's [MCP](https://about.gitlab.com/topics/ai/model-context-protocol/) support, you can now connect Jira or any tool that supports MCP directly to your AI-powered development environment. Query issues, update tickets, and sync your workflow — all through natural language, without ever leaving your IDE.\n\n## What you'll learn\n\nIn this tutorial, we'll walk you through:\n\n* **Setting up the Jira/Atlassian OAuth application** for secure authentication\n* **Configuring GitLab Duo Agent Platform** as an MCP client\n* **Three practical use cases** demonstrating real-world workflows\n\n## Prerequisites\n\nBefore getting started, ensure you have the following:\n\n| Requirement | Details |\n| ---- | ----- |\n| **GitLab instance** | GitLab 18.8+ with Duo Agent Platform enabled |\n| **Jira account** | Jira Cloud instance with admin access to create OAuth applications |\n| **IDE** | Visual Studio Code with GitLab Workflow extension installed |\n| **MCP support** | MCP support enabled in GitLab |\n\n\n## Understanding the architecture\n\nGitLab Duo Agent Platform acts as an **MCP client**, connecting to the Atlassian MCP server to access your Jira project management data. Atlassian  MCP server handles authentication, translates natural language requests into API calls, and returns structured data back to GitLab Duo Agent Platform — all while maintaining security and audit controls.\n\n## Part 1: Configure Jira OAuth application\n\nTo securely connect GitLab Duo Agent Platform to your Jira instance, you'll need to create an OAuth 2.0 application in the Atlassian Developer Console. This grants to GitLab the MCP server authorized access to your Jira data.\n\n### Setup steps\n\nIf you prefer to configure manually, follow these steps:\n\n1. **Navigate to the Atlassian Developer Console**\n\n   * Go to [developer.atlassian.com/console/myapps](https://developer.atlassian.com/console/myapps)\n\n   * Sign in with your Atlassian account\n\n2. **Create a new OAuth 2.0 app**\n\n   * Click **Create** → **OAuth 2.0 integration**\n\n   * Enter a name (e.g., \"gitlab-dap-mcp\")\n\n   * Accept the terms and click **Create**\n\n3. **Configure permissions**\n\n   * Navigate to **Permissions** in the left sidebar.\n\n   * Add **Jira API** and configure the following scopes:\n\n     * `read:jira-work` — Read issues, projects, and boards\n\n     * `write:jira-work` — Create and update issues\n\n     * `read:jira-user` — Read user information\n\n4. **Set up authorization**\n\n   * Go to **Authorization** in the left sidebar\n\n   * Add a callback URL for your environment (`https://gitlab.com/oauth/callback`)\n\n   * Save your changes\n\n5. **Retrieve credentials**\n\n   * Navigate to **Settings**\n\n   * Copy your **Client ID** and **Client Secret**\n\n   * Store these securely — you'll need them for the MCP configuration\n\n\n### Interactive walkthrough: Jira OAuth setup\n\nClick on the image below to get started.\n\n\n[![Jira OAuth setup tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772644850/wnzfoq43nkkfmgdqldmr.png)](https://gitlab.navattic.com/jira-oauth-setup)\n\n\n## Part 2: Configure GitLab Duo Agent Platform MCP client\n\nWith your OAuth credentials ready, you can now configure GitLab Duo Agent Platform to connect to the Atlassian MCP server.\n\n### Create your MCP configuration file\n\nCreate the MCP configuration file in your GitLab project at `.gitlab/duo/mcp.json`:\n\n\n```json\n{\n  \"mcpServers\": {\n    \"atlassian\": {\n      \"type\": \"http\",\n      \"url\": \"https://mcp.atlassian.com/v1/mcp\",\n      \"auth\": {\n        \"type\": \"oauth2\",\n        \"clientId\": \"YOUR_CLIENT_ID\",\n        \"clientSecret\": \"YOUR_CLIENT_SECRET\",\n        \"authorizationUrl\": \"https://auth.atlassian.com/oauth/authorize\",\n        \"tokenUrl\": \"https://auth.atlassian.com/oauth/token\"\n      },\n      \"approvedTools\": true\n    }\n  }\n}\n```\n\nReplace `YOUR_CLIENT_ID` and `YOUR_CLIENT_SECRET` with the credentials you generated in Part 1.\n\n### Enable MCP in GitLab\n\n1. Navigate to your **Group Settings** → **GitLab Duo** → **Configuration**\n2. Make sure “Allow external MCP tools” is checked\n\n### Verify the connection\n\nOpen your project in VS Code and ask in GitLab Duo Agent Platform chat:\n\n```text\nWhat MCP tools do you have access to?\n```\n\nThen\n\n```text\nTest the MCP JIRA configuration in this project\n```\n\nAt this point you'll be redirected from the IDE to the MCP Atlassian website to approve access:\n\n![Redirect to MCP Atlassian website](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/z5acqjgguh0damnnde9g.png \"Redirect to MCP Atlassian website\")\n\n\u003Cbr>\u003C/br>\n\n![Approve access](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/rwowamm8nsubhpixtn3i.png \"Approve access\")\n\n\u003Cbr>\u003C/br>\n\n![Select your JIRA instance and approve](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/chuzqd0jeptfwvoj7wjr.png \"Select your JIRA instance and approve\")\n\n\u003Cbr>\u003C/br>\n\n![Success!](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/bsgti5iste2bzck19o5y.png \"Success!\")\n\n\u003Cbr>\u003C/br>\n\n### Verify with the MCP Dashboard\n\nGitLab also provides a built-in **MCP Dashboard** directly in your IDE for this.\n\nIn VS Code or VSCodium, open the Command Palette (`Cmd+Shift+P` on macOS, `Ctrl+Shift+P` on Windows/Linux) and search for **\"GitLab: Show MCP Dashboard\"**. The dashboard opens in a new editor tab and gives you:\n\n* **Connection status** for each configured MCP server\n* **Available tools** exposed by the server (e.g., `jira_get_issue`, `jira_create_issue`)\n* **Server logs** so you can see exactly which tools are being called in real time\n\n![MCP servers dashboard and status](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/mmvdfchucacsydivowvn.png \"MCP servers dashboard and status\")\n\n\u003Cbr>\u003C/br>\n\n![Server details and permissions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/tcocgdvovp2dl42pvfn8.png \"Server details and permissions\")\n\n\u003Cbr>\u003C/br>\n\n\n![MCP Server logs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643466/mougvqqk1bozchaufsci.png \"MCP Server logs\")\n\n\u003Cbr>\u003C/br>\n\n### Interactive walkthrough: Testing MCP\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005495?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Testing MCP\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Part 3: Use cases in action\n\nNow that your integration is configured, let's explore three practical workflows that demonstrate the power of connecting Jira to GitLab Duo Agent Platform.\n\n### Planning assistant\n\n**Scenario:** You're preparing for sprint planning and need to quickly assess the backlog, understand priorities, and identify blockers.\n\nThis demo shows you how to:\n\n* Query the backlog\n* Identify unassigned high-priority issues\n* Get AI-powered sprint recommendations\n\n#### Example prompts\n\nTry these prompts in GitLab Duo Agent Platform Chat:\n\n```text\nList all the unassigned issues in JIRA for project GITLAB\n```\n\n```text\nSuggest the two top issues to prioritize and summarize them. Assign them to me.\n```\n\n### Interactive walkthrough: Project planning\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005462?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Project Planning\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player. js\">\u003C/script>\n\n### Issue triage and creation from code\n\n**Scenario:** While reviewing code, you discover a bug and want to create a Jira issue with relevant context — without leaving your IDE.\n\nThis demo walks you through:\n\n* Identifying a bug while coding\n* Creating a detailed Jira issue via natural language\n* Auto-populating issue fields with code context\n* Linking the issue to your current branch\n\n#### Example prompts\n\n```text\nSearch in JIRA for a bug related to: Null pointer exception in PaymentService.processRefund().\nIf it does not exist create it with all the context needed from the code. Find possible blockers that this bug may cause.\n```\n\n```text\nCreate a new branch called issue-gitlab-18, checkout, and link it to the issue we just created. Assign the JIRA issue to me and mark it as in-progress.\n```\n\n### Interactive walkthrough: Bug review and task automation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005368?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Bug Review\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Cross-system incident investigation\n\n**Scenario:** A production incident occurs, and you need to correlate information from Jira (incident ticket), GitLab Project Management, your codebase, and merge requests to identify the root cause.\n\nThis demo demonstrates:\n\n* Fetching incident details from Jira\n* Correlating with recent merge requests in GitLab\n* Identifying potentially related code changes\n* Generating an incident timeline\n* Design a remediation plan and create it as a work item in GitLab\n\n#### Example prompts\n\n```text\n\"We have a production incident INC-1 about checkout failures. Can you help me investigate with all available context?\"\n```\n\n```text\nCreate a timeline of events for incident INC-1 including related Jira issues and recent deployments\n```\n\n```text\nPropose a remediation plan\n```\n\n### Interactive walkthrough: Cross-system troubleshooting and remediation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005413?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Cross System Investigation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Troubleshooting\n\nThese are some common setup issues and quick fixes:\n\n| Issue | Solution |\n| ----- | ----- |\n| \"MCP server not found\" | Verify the `mcp.json` file is in the correct location and properly formatted |\n| \"Authentication failed\" | Re-check your OAuth credentials and ensure scopes are correctly configured in Atlassian |\n| \"No Jira tools available\" | Restart VS Code after updating `mcp.json` and ensure MCP is enabled in GitLab |\n| \"Connection timeout\" | Check your network connectivity to `mcp.atlassian.com` |\n\n\u003Cbr/> For detailed troubleshooting, see the [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/).\n\n\n## Security considerations\n\nWhen integrating Jira with GitLab Duo Agent Platform:\n\n* **OAuth tokens** — Make sure credentials remain secure\n* **Principle of least privilege** — Only grant the minimum required Jira scopes\n* **Token rotation** — Regularly rotate your OAuth credentials as part of security hygiene\n\n\n## Summary\n\nConnecting GitLab Duo Agent Platform to different tools through MCP transforms how you interact with your development lifecycle. In this article, you have learned how to:\n\n* **Query issues naturally** — Ask questions about your backlog, sprints, and incidents in natural language.\n* **Create and update issues on all your DevSecOps environment** — File bugs and update tickets without leaving your IDE.\n* **Correlate across systems** — Combine Jira data with GitLab project management, merge requests, and pipelines for complete visibility.\n* **Reduce context switching** — Keep your focus on code while staying connected to project management.\n\nThis integration exemplifies the power of MCP: standardized, secure access to your tools through AI, enabling developers to work more efficiently without sacrificing governance or security.\n\n\n## Read more\n\n* [GitLab Duo Agent Platform adds support for Model Context Protocol](https://about.gitlab.com/blog/duo-agent-platform-with-mcp/)\n\n* [What is Model Context Protocol?](https://about.gitlab.com/topics/ai/model-context-protocol/)\n\n* [Agentic AI guides and resources](https://about.gitlab.com/blog/agentic-ai-guides-and-resources/)\n\n* [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/)\n\n* [Get started with GitLab Duo Agent Platform: The complete guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)",{"featured":12,"template":13,"slug":742},"extend-gitlab-duo-agent-platform-connect-any-tool-with-mcp",{"content":744,"config":754},{"title":745,"description":746,"authors":747,"heroImage":749,"date":750,"body":751,"category":9,"tags":752},"10 AI prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[748],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772632341/duj8vaznbhtyxxhodb17.png","2026-03-04","AI-assisted coding tools are helping developers generate code faster than ever. So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[26,753],"DevOps platform",{"featured":12,"template":13,"slug":755},"10-ai-prompts-to-speed-your-teams-software-delivery",{"promotions":757},[758,771,782,794],{"id":759,"categories":760,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[728,37],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":786,"text":762,"button":787,"image":791},"security-modernization",[785],"security","Are you trading speed for security?",{"text":788,"config":789},"Get your security maturity score",{"href":790,"dataGaName":767,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":795,"paths":796,"header":799,"text":800,"button":801,"image":806},"github-azure-migration",[797,798],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":802,"config":803},"See how GitLab compares to GitHub",{"href":804,"dataGaName":805,"dataGaLocation":245},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":807},{"src":781},{"header":809,"blurb":810,"button":811,"secondaryButton":816},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":812,"config":813},"Get your free trial",{"href":814,"dataGaName":52,"dataGaLocation":815},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":507,"config":817},{"href":56,"dataGaName":57,"dataGaLocation":815},1776447709402]