What Is a Continuous Delivery Maturity Model CDMM?

Triggering integration tests in your Continuous Delivery pipeline. Automatically building your software to shorten the development cycle. Software Deployment Fix deployment problems using modern strategies and best practices. Continuous Delivery Understand delivery, deployment, pipelines, and GitOps. I like the idea a lot and would like to use that model for us to evaluate our own maturity.

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  • Laying the foundations for these elements early on makes it much easier to keep progressing as you solve the technical challenges.
  • After evaluating your organization according to the model you need to set the goals and identify which practices will give your organization the best outcomes.

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Infrastructure as Code Maturity Model

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You don’t have to immediately move all of your processes from one level to another. You can gradually implement these practices to help improve the automation of your ML system development and production. 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. To automate the process of using new data to retrain models in production, you need to introduce automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management.


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 , a five-level evolutionary path of increasingly organized and systematically more mature software development processes. We’ve put together a high-level CI / CD Maturity guide to help with these challenges. We list all the processes and practices that need to be in place before you can truly claim that you have made Continuous Deployments possible. The guide makes certain basic assumptions i.e. it assumes your code is managed in a version control system.

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MLOps https://forexhero.info/ 0 is common in many businesses that are beginning to apply ML to their use cases. This manual, data-scientist-driven process might be sufficient when models are rarely changed or trained. In practice, models often break when they are deployed in the real world. The models fail to adapt to changes in the dynamics of the environment, or changes in the data that describes the environment.

Weave GitOps Core: Continuous declarative delivery

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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. Using a continuous deliverymaturity model can facilitate discussions on what you want to achieve with CI/CD and will help you map out a step-by-step approach to implementing the various elements. Depending on your organization, your end goal may be to have changes deployable within a day . Or your goal may be to achieve continuous deployment, with updates being shipped if they pass all stages of the pipeline successfully. You can also use continuous feedback from production to inform hypothesis-driven development .

The most effective improvement processes, whether they streamline manufacturing operations or speed up software development, describe the path to desired improvements — not just the end state. Continuous improvement processes never focus on the end state, because perfection, however it’s defined, can only be incrementally approached, never fully achieved. Health monitoring for applications and environments and proactive handling of problems. Continuous Delivery presents a compelling vision of builds that are automatically deployed and tested until ready for production. While there is no single standard for CDMM, most models proposed in the industry consist of five levels, with Level 1 being the lowest level of maturity and Level 5 being the highest. Each level represents a set of capabilities that an organization must have in order to achieve that level of maturity.

For continuous training, the automated ML training pipeline can fetch a batch of the up-to-date feature values of the dataset that are used for the training task. Make code reproducible between development and production environments. CI is no longer only about testing and validating code and components, but also testing and validating data, data schemas, and models.

Another characteristic of advanced continuous delivery maturity is the use of quantitative measures of software performance and quality, along with metrics that track the health and consistency of the CD process. Identify and monitor key performance indicators for better control over software acceptance and rollback criteria in test and in live production. For example, continually monitored application performance KPIs enable an CD system to automatically roll back a release that exhibits problems in production. Advanced practices include fully automatic acceptance tests and maybe also generating structured acceptance criteria directly from requirements with e.g. specification by example and domains specific languages. This means no manual testing or verification is needed to pass acceptance but typically the process will still include some exploratory testing that feeds back into automated tests to constantly improve the test coverage and quality. If you correlate test coverage with change traceability you can start practicing risk based testing for better value of manual exploratory testing.

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It can also be difficult to figure out how the team is progressing on this journey. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in their teams. Most companies already have some data gathering in place or have a customer feedback loop to track how their software is perceived by users. Continuous Intelligence is the automation of this software user tracking process, to enable software companies in developing software features that add the most value.

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Delivering new software is the single most important function of businesses trying to compete today. Many companies get stuck with flaky scripting, manual interventions, complex processes, and large unreliable tool stacks across diverse infrastructure. Software teams are left scrambling to understand their software supply chain and discover the root cause of failures. 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 the base level in this category it is important to establish some baseline metric for the current process, so you can start to measure and track.

  • Continuous Intelligence is the automation of this software user tracking process, to enable software companies in developing software features that add the most value.
  • The following figure is a schematic representation of an automated ML pipeline for CT.
  • Microservices Best practices for building loosely coupled services.
  • Making sure that you test your model for deployment, including infrastructure compatibility and consistency with the prediction service API.
  • Depending on your organization, your end goal may be to have changes deployable within a day .

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