Climb the five steps of a continuous delivery maturity model

At intermediate level, builds are typically triggered from the source control system on each commit, tying a specific commit to a specific build. Tagging and versioning of builds is automated and the deployment process is standardized over all environments. Built artifacts or release packages are built only once and are designed to be able to be deployed in any environment. The standardized deployment process will also include a base for automated database deploys (migrations) of the bulk of database changes, and scripted runtime configuration changes. A basic delivery pipeline is in place covering all the stages from source control to production.

„Cross-organizational training and automation, of as many processes as possible, also will be key.“ The data analysis step is still a manual process for data scientists before
the pipeline starts a new iteration of the experiment. One of the main principles of CI/CD is to integrate changes into the primary shared repository early and often. This helps avoid costly integration problems down the line when multiple developers attempt to merge large, divergent, and conflicting changes into the main branch of the repository in preparation for release. Typically, CI/CD systems are set to monitor and test the changes committed to only one or a few branches.

Maturity and beyond

You also submit the tested source code for the pipeline to
the IT team to deploy to the target environment. This setup is suitable when
you deploy new models based on new data, rather than based on new ML ideas. Continuous integration is the practice of integrating, building, testing and delivering functional software on a scheduled, repeatable and automated basis. This document provides guidance to create a CI pipeline required for software development teams to implement continuous delivery and DevOps. The next level in the continuous delivery maturity model entails defining the activities for the entire move-to-production process, along with the file and system locations plus tooling to automate it.

  • Tagging and versioning of builds is structured but manual and the deployment process is gradually beginning to be more standardized with documentation, scripts and tools.
  • Failures in a CI/CD pipeline are immediately visible and halt the advancement of the affected release to later stages of the cycle.
  • At expert level, some organizations will evolve the component based architecture further and value the perfection of reducing as much shared infrastructure as possible by also treating infrastructure as code and tie it to application components.
  • By following these best practices, organizations can implement a CDMM that helps them to achieve higher levels of maturity and to deliver software changes quickly and reliably, with minimal risk and downtime.
  • Significant differences between staging and production can allow problematic changes to be released that were never observed to be faulty in testing.
  • The goal of level 1 is to perform continuous training of the model by
    automating the ML pipeline; this lets you achieve continuous delivery of model
    prediction service.
  • While integration tests are component specific, acceptance tests typically span over several components and across multiple systems.

This automated CI/CD system lets your data
scientists rapidly explore new ideas around feature engineering, model
architecture, and hyperparameters. They can implement these ideas and
automatically build, test, and deploy the new pipeline components to the target
environment. To address the challenges of this manual process, MLOps practices for CI/CD
and CT are helpful.

CI/CD pipeline architecture on azure devops

This manual, data-scientist-driven process might be sufficient
when models are rarely changed or trained. The models fail to adapt to changes in the
dynamics of the environment, or changes in the data that describes the
environment. For more information, see
Why Machine Learning Models Crash and Burn in Production.

ci cd maturity model

Setting up VPNs or other network access control technology is recommended to ensure that only authenticated operators are able to access your system. Depending on the complexity of your network topology, your CI/CD system may need to access several different networks to deploy code to different environments. There are some straightforward steps you can take to improve speed, like scaling out your CI/CD infrastructure and optimizing tests. However, as time goes on, you may be forced to make critical decisions about the relative value of different tests and the stage or order where they are run. Sometimes, paring down your test suite by removing tests with low value or with indeterminate conclusions is the smartest way to maintain the speed required by a heavily used pipelines. CI/CD has many potential benefits, but successful implementation often requires a good deal of consideration.

An Introduction to CI/CD Best Practices

Usually test involves verifying expected functionality according to requirements in different ways but we also want to emphasize the importance of verifying the expected business value of released features. The design and architecture of your products and services will have an essential impact on your ability to adopt continuous delivery. If a system is built with continuous delivery principles and a rapid release mind set from the beginning, the journey will be much smoother. However, an upfront complete redesign of the entire system is not an attractive option for most organizations, which is why we have included this category in the maturity model. To realize these advantages, however, you need to ensure that every change to your production environment goes through your pipeline.


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By deploying an ML training pipeline, you can enable
CT, and you can set up a CI/CD system to
rapidly test, build, and deploy new implementations of the ML pipeline. This document is for data scientists and ML engineers who want to apply
principles to ML systems (MLOps). MLOps is an ML engineering culture and
practice that aims at unifying ML system development (Dev) and ML system
operation (Ops). Practicing MLOps means that you advocate for automation and
monitoring at all steps of ML system construction, including integration,
testing, releasing, deployment and infrastructure management.

Continuous Delivery Model

This guideline helps prevent problems that arise when software is compiled or packaged multiple times, allowing slight inconsistencies to be injected into the resulting artifacts. Building the software separately at each new stage ci cd maturity model can mean the tests in earlier environments weren’t targeting the same software that will be deployed later, invalidating the results. CI/CD systems should be deployed to internal, protected networks, unexposed to outside parties.

ci cd maturity model

Test automation tools include pipeline software like Jenkins; test automation systems like Selenium or Cypress; and cloud services, including AWS CodePipeline or Microsoft Azure DevTest Labs. Containers are a common runtime destination for CI/CD pipelines, and if they’re in use at this first stage of the continuous delivery maturity model, development teams have usually adopted Docker images defined by a Dockerfile. A Continuous Delivery Maturity Model (CDMM) is a framework for assessing an organization’s maturity in implementing continuous delivery practices.

CI/CD pipeline design and build for a banking application on Azure Devops.

When moving to beginner level you will naturally start to investigate ways of gradually automating the existing manual integration testing for faster feedback and more comprehensive regression tests. For accurate testing the component should be deployed and tested in a production like environment with all necessary dependencies. The purpose of the maturity model is to highlight these five essential categories, and to give you an understanding of how mature your company is.

ci cd maturity model

Another benefit of containerized testing environments is the portability of your testing infrastructure. With containers, developers have an easier time replicating the configuration that will be used later on in the pipeline without having to either manually set up and maintain infrastructure or sacrifice environmental fidelity. Since containers can be spun up easily when needed and then destroyed, users can make fewer compromises with regard to the accuracy of their testing environment when running local tests. In general, using containers locks in some aspects of the runtime environment to help minimize differences between pipeline stages. To help ensure that your tests run the same at various stages, it’s often a good idea to use clean, ephemeral testing environments when possible.

The 4 stages of DevSecOps maturity

Part of what makes it possible for CI/CD to improve your development practices and code quality is that tooling often helps enforce best practices for testing and deployment. Promoting code through your CI/CD pipelines requires each change to demonstrate that it adheres to your organization’s codified standards and procedures. Failures in a CI/CD pipeline are immediately visible and halt the advancement of the affected release to later stages of the cycle. This is a gatekeeping mechanism that safeguards the more important environments from untrusted code. Continuous integration, delivery, and deployment, known collectively as CI/CD, is an integral part of modern development intended to reduce errors during integration and deployment while increasing project velocity.

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