With this model we aim to be broader, to extend the concept beyond automation and spotlight all the key aspects you need to consider for a successful Continuous Delivery implementation across the entire organization. Some organizations may incorporate this specific phase as a preparatory stage to implement a https://www.globalcloudteam.com/ defined plan. In this early stage, development and operations teams often work separately. Lots of factors in a DevOps model feed into overall improvements in innovation. Mature DevOps teams spend less time on manual processes, are more open and collaborative, and feel more comfortable with experimentation.
Interesting metrics can e.g. be cycle-time, delivery time, number of releases, number of emergency fixes, number of incidents, number of features per release, bugs found during integration test etc. 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. Your assessment will give you a good base when planning the continuous delivery maturity model implementation of Continuous Delivery and help you identify initial actions that will give you the best and quickest effect from your efforts. The model will indicate which practices are essential, which should be considered advanced or expert and what is required to move from one level to the next. However, you need to try new ML ideas and rapidly deploy new implementations
of the ML components.
How can you assess the maturity of your continuous delivery process?
Parallel software deployment environments don’t require cloud services, but they are much easier to set up when infrastructure is delivered instantly as a service. Cloud services and CD automation simplify the task to create and manage redundant environments for production, beta and developer code. New releases nondisruptively roll into production after a suitable testing cycle with the help of parallel setups.
Multiple processes are consolidated and all changes, bugs, new features, emergency fixes, etc, follow the same path to production. Decisions are decentralized to the team and component ownership is defined which gives teams the ability to build in quality and to plan for sustainable product and process improvements. For a rapid and reliable update of the pipelines in production, you need a
robust automated CI/CD system. 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
DevOps Roadmap 2022: How to Become a DevOps Engineer in 8 Steps
DevOps maturity results in a faster time-to-market for products, bug fixes, and other updates. Since DevOps focuses on breaking down the walls between different areas of development and operations teams, collaboration and continuous improvement results in increased speed across all processes. Since DevOps is a model for the development and deployment of software, measuring DevOps maturity involves the assessment of practices across multiple teams, processes, metrics, and technologies.
Techniques for zero downtime deploys can be important to include in the automated process to gain better flexibility and to reduce risk and cost when releasing. At this level you might also explore techniques to automate the trailing part of more complex database changes and database migrations to completely avoid manual routines for database updates. At beginner level, the monolithic structure of the system is addressed by splitting the system into modules. Modules give a better structure for development, build and deployment but are typically not individually releasable like components. Doing this will also naturally drive an API managed approach to describe internal dependencies and also influence applying a structured approach to manage 3rd party libraries. At this level the importance of applying version control to database changes will also reveal itself.
Build & Deploy
At this advanced level, teams also tackle harder deployment problems, such as multi-tier applications in which several components must deploy together, but are on different release cycles. These composite applications also include more sophisticated components, notably databases, that are complicated to deploy and test. A maturity model describes milestones on the path of improvement for a particular type of process. In the IT world, the best known of these is the capability maturity model (CMM), a five-level evolutionary path of increasingly organized and systematically more mature software development processes. Continuous Delivery 3.0 Maturity Model (CD3M) is a framework for assessing an organization’s maturity in implementing continuous delivery practices, created by the Netherlands National Institute for the Software Industry (NISI).
- 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.
- 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.
- These composite applications also include more sophisticated components, notably databases, that are complicated to deploy and test.
- However, you need to try new ML ideas and rapidly deploy new implementations
of the ML components.
- Interesting metrics can e.g. be cycle-time, delivery time, number of releases, number of emergency fixes, number of incidents, number of features per release, bugs found during integration test etc.
This overall culture leads to a motivated development team that engages in idea-sharing and continuous improvement, leading to more innovation and, ultimately, better products. Eric Minick is a lead consultant at UrbanCode where he helps customers implement continuous delivery. Eric has been at the forefront of continuous integration and delivery for 8+ years as a developer, tester and consultant. Eric Minick discusses continuous delivery challenges in the enterprise where large projects, distributed teams or strict governance requirements have resulted in increased automation efforts throughout the life cycle.
The Continuous Delivery Maturity Model
It can help organizations identify initial actions that provide the most significant effect, while indicating which practices are essential, and which should be considered advanced or expert. CDMM provides a structured way for organizations to assess and improve their ability to implement continuous delivery practices, which can lead to increased efficiency, quality, and stakeholder satisfaction. At beginner level, you start to measure the process and track the metrics for a better understanding of where improvement is needed and if the expected results from improvements are obtained. Reporting at this stage would typically include static analysis of code and quality reports which might be scheduled so that the latest reports are always accessible to facilitate decisions on quality and where improvements are needed.
Advanced CD implementations have almost completely automated code’s journey from integration testing through various stages of test deployments onto production systems. So, if the entire CD process can launch with one command, why are there still two higher levels of CD maturity? Although testing is automated, many organizations are reluctant to cede control over the release to production, and, thus, might require a manual approval step before code gets promoted to the next stage of deployment. 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. The goal is to increase release cycles’ consistency, not their speed, although the intermediate stage is typically when organizations can stick to regular releases on a defined schedule, such as nightly or weekly. The entire CD process should be automated, launched with a single command.
Based on your analysis and benchmarking, you need to plan and prioritize your actions to improve your CD maturity. You should focus on the areas that have the most impact on your goals and outcomes. You should also consider the feasibility, cost, and effort of each action. You should create a roadmap that defines your vision, objectives, milestones, and deliverables.
In any ML project, after you define the business use case and establish the
success criteria, the process of delivering an ML model to production involves
the following steps. These steps can be completed manually or can be completed
by an automatic pipeline. To develop and operate complex systems like these, you can apply DevOps
principles to ML systems (MLOps). This document covers concepts to consider when
up an MLOps environment for your data science practices, such as CI, CD, and CT
in ML. Therefore, many businesses are investing in their data science teams and ML
capabilities to develop predictive models that can deliver business value to
However, it is also important to constantly increase the test-coverage of the application to build up the confidence in speed with frequent releases. 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. At this stage it might also become necessary to scale out the build to multiple machines for parallel processing and for specific target environments.