In this category we want to show the importance of handling this information correctly when adopting Continuous Delivery. Information must e.g. be concise, relevant and accessible at the right time to the right persons in order to obtain the full speed and flexibility possible with Continuous Delivery. Apart from information directly used to fulfill business requirements by developing and releasing features, it is also important to have access to information needed to measure the process itself and continuously improve it. The CDMM can be used to identify areas for improvement and guide an organization’s efforts to implement continuous delivery practices.
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. 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.
A structured approach
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. If there are practices you do not want to adopt you need to analyse the consequences of excluding them. It is also important to decide on an implementation strategy, you can e.g. start small using slack in the existing process to improve one thing at a time. However, from our experience you will have a better chance of a successful implementation if you jump start the journey with a dedicated project with a clear mandate and aggressive goals on e.g. reducing cycle time.
Moving to beginner level, teams stabilize over projects and the organization has typically begun to remove boundaries by including test with development. Multiple backlogs are naturally consolidated into one per team and basic agile methods are adopted which gives stronger teams that share the pain when bad things happen. The following diagram shows the implementation of the ML pipeline using CI/CD,
which has the characteristics of the automated ML pipelines setup plus the
automated CI/CD routines. In addition to offline model validation, a newly deployed model
undergoes online model validation—in a canary deployment or an A/B testing
setup—before it serves prediction for the online traffic. To develop and operate complex systems like these, you can apply DevOps
principles to ML systems (MLOps).
Services & Support
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. GitOps has emerged as a key technology in the cloud native computing space over the last few years. Research into delivery velocity (from DORA and others) has shown that speeding up software delivery is closely correlated with business success.
- 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.
- The Maturity Model guides the improvements of Continuous Delivery pipelines and/or software development processes in software organizations.
- DBA, CM and Operations are beginning to be a part of the team or at least frequently consulted by the team.
- 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.
- These steps can be completed manually or can be completed
by an automatic pipeline.
Similar to Build & Deploy, maturity in this category will involve tools and automation. 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.
MLOps: Continuous delivery and automation pipelines in machine learning
The lowest maturity level is sometimes called the initial or regressive state because it is highly inefficient. At this stage, when automation is applied to application delivery, it’s often ad hoc and isolated — usually instituted by a single workgroup or developer and focused on a particular problem. Nevertheless, organizations starting down the continuous delivery path have often standardized http://cofepublic.net/?rz=kp portions of software development, such as the build system using CMake, Microsoft Visual Studio or Apache Ant and a code repository, like GitHub. The journey that started with the Agile movement a decade ago is finally getting a strong foothold in the industry. Business leaders now have begun to embrace the fact that there is a new way of thinking about software development.
Each level represents a set of capabilities that an organization must have in order to achieve that level of maturity. The data analysis step is still a manual process for data scientists before
the pipeline starts a new iteration of the experiment. 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. Andreas Rehn is an Enterprise Architect and a strong advocate for Continuous Delivery, DevOps, Agile and Lean methods in systems development.
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. Expert practices will include zero touch continuous deployment to production where every commit can potentially make it all the way to production automatically. A typical organization will have one or more legacy systems of monolithic nature in terms of development, build and release. Many organizations at the base maturity level will have a diversified technology stack but have started to consolidate the choice of technology and platform, this is important to get best value from the effort spent on automation. MLOps level 0 is common in many businesses that are beginning to apply ML to
their use cases.
Structuring Continuous Delivery implementation into these categories that follows a natural maturity progression will give you a solid base for a fast transformation with sustainable results. NISI has recently released the Continuous Delivery 3.0 maturity model, or CD3M. The Maturity Model guides the improvements of Continuous Delivery pipelines and/or software development processes in software organizations. The CD3M maturity model has five levels from Foundation level (1) towards Expert level (5).