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 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, 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. This document is for data scientists and ML engineers who want to apply
DevOps
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 Maturity Model Examples
They can implement these ideas and
automatically build, test, and deploy the new pipeline components to the target
environment. Testing illustrates the inherent overlap between continuous integration and continuous delivery; consistency demands that software passes acceptance tests before it is promoted to production. Test automation tools include pipeline software like Jenkins; test automation systems like Selenium http://cofepublic.net/?rz=kp or Cypress; and cloud services, including AWS CodePipeline or Microsoft Azure DevTest Labs. To maintain a consistent release train, the team must automate test suites that verify software quality and use parallel deployment environments for software versions. Automation brings the CI/CD approach to unit tests, typically during the development stage and integration stage when all modules are brought together.
- Continuous Delivery is all about seeing the big picture, to consider all aspects that affect the ability to develop and release your software.
- A project can focus on producing requirements for one or multiple teams and when all or enough of those have been verified and deployed to production the project can plan and organize the actual release to users separately.
- While every organization is different, a number of common patterns have emerged.
- The journey that started with the Agile movement a decade ago is finally getting a strong foothold in the industry.
- 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.
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 following section discusses the typical steps for training and evaluating
an ML model to serve as a prediction service. While every organization is different, a number of common patterns have emerged. The GitOps Maturity Model shows a simple four step process that organizations commonly transition through as they move from using GitOps to manage single clusters and applications to managing large scale deployments of hundreds or even thousands of clusters. Tobias Palmborg, Believes that Continuous Delivery describes the vision that scrum, XP and the agile manifesto once set out to be. Continuous Delivery is not just about automating the release pipeline but how to get your whole change flow, from grain to bread ,in a state of the art shape.
MLOps level 0: Manual process
It is designed to guide organizations in their efforts to improve their software development process and ultimately achieve continuous delivery. 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.
Where we visualize and understand the path from idea to where it is released and brings business value. This is why we created the Continuous Delivery Maturity Model, to give structure and understanding to the implementation of Continuous Delivery and its core components. 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. 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. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community.