Testing is without doubt very important for any software development operation and is an absolutely crucial part of a successful implementation of Continuous Delivery. 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.

In a nutshell, while both CMM and CMMI aim to enhance organizational processes, CMMI offers a more comprehensive and adaptable framework, making it suitable for a broader range of industries and organizational needs beyond software development. The goal of this guide is to first and foremost highlight the practices required for CD. The tools simply help with the adoption of the practice; the simple rule being that we should never build a process or practice around a tool, the tool must rather make the process or practice easier or more efficient. To truly reach the CD zenith software engineers really have to turn all the IT “dials” to the max. For teams just embarking on the CD journey, it can be a daunting task to try and make sense of all the frameworks, practices, tools, buzzwords and hype out there.

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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. At this level reporting is typically done manually and on-demand by individuals. 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. At the advanced level you will have split the entire system into self contained components and adopted a strict api-based approach to inter-communication so that each component can be deployed and released individually. With a mature component based architecture, where every component is a self-contained releasable unit with business value, you can achieve small and frequent releases and extremely short release cycles.

• It serves as a translator, ensuring that project goals and team efforts align harmoniously, leading to smoother collaboration and reduced misunderstandings.
• The CMMI was developed by the Software Engineering Institute at Carnegie Mellon University as a process improvement tool for projects, divisions, or organizations.
• A basic delivery pipeline is in place covering all the stages from source control to production.
• This automated CI/CD system lets your data
  scientists rapidly explore new ideas around feature engineering, model
  architecture, and hyperparameters.
• 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.

Amplifying feedback can help you catch failures before they make it downstream, and accelerate your time to resolution. One easy way to speed up feedback is by automating notifications so that teams are alerted to incidents or bugs when they happen. See how Atlassian’s Site Reliability Engineers do incident management and practice ChatOps for conversation-driven development. To do so, you need a strong continuous integration pipeline that tests, packages, and delivers your releases.

Unraveling the Difference between Middle and Senior Software Developers

Verifying expected business value of changes becomes more natural when the organization, culture and tooling has reached a certain maturity level and feedback of relevant business metrics is fast and accessible. As an example the implementation of a new feature must also include a way to verify the expected business result by making sure the relevant metrics can be pulled or pushed from the application. The definition of done must also be extended from release to sometime later when business has analyzed the effects of the released continuous integration maturity model feature or change.. At the base stage in the maturity model a development team or organization will typically practice unit-testing and have one or more dedicated test environments separate from local development machines. This system and integration level testing is typically done by a separate department that conducts long and cumbersome test periods after development “code freeze”. The design and architecture of your products and services will have an essential impact on your ability to adopt continuous delivery.

Continuous delivery is a widespread software delivery practice used by IT companies to provide custom functions in a faster, safer, and more permanent way. However it is the logical conclusion of Continuous Delivery where the release to production is completely automated. This means that every commit to the workspace is automatically released to production, and thus leading to several deployments of your software during a day.

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In addition, production deployment of a new version of an ML model
usually goes through A/B testing or online experiments before the model is
promoted to serve all the prediction request traffic. 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.

Every step strives toward improving efficiency, quality, and overall performance. Understanding these limitations helps us use CMM more effectively within its defined scope. It’s like recognizing the edges of a puzzle—knowing where the pieces fit and where they don’t, so you can complete the overall picture using the right tools and strategies for different business and project management aspects. The list is quite intimidating so we’ve highlighted the practices we think you should focus on when starting on this journey.

Jump start the journey

Each CMMI model is designed to be used in concert with other CMMI models, making it easier for organizations to pursue enterprise-wide process improvement at their own pace. This CMMI model has a continuous representation, which focuses on measuring process improvement using capability levels. Capability levels apply to process-improvement achievement within individual process areas such as configuration management or verification.

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. DevOps isn’t a destination, it’s a journey towards a frequent and more reliable release pipeline, automation and stronger collaboration between development, IT and business teams. This maturity model is designed to help you assess where your team is on their DevOps journey.

Evolution of CMMI

One of the first considerations a PM needs to address is the project team’s Release Management Maturity. Thus, developers need the continuous delivery model for running tests and deploying/releasing. The continuous delivery branching model, for example, allows the developers to run tests freely and make changes without destroying the main code line. The developers can develop, test, and modify the code in parallel or isolation and then merge it to a master. The idea allows one to run various types of tests at each stage and complete it by launching with the deployment of the system in the actual product that end-users see.

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. 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 (KPIs) 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. A maturity model describes milestones on the path of improvement for a particular type of process.

More from Ryan Krull and Standard Bank Engineering

Explore the possibility to hire a dedicated R&D team that helps your company to scale product development. Arun Gupta is Director of Developer Advocacy at Red Hat and focuses on JBoss Middleware. As a founding member of the Java EE team at Sun Microsystems, he spread the love for technology all around the world. At Oracle, he led a cross-functional team to drive the global launch of the Java EE 7 platform through strategy, planning, and execution of content, marketing campaigns, and program. He is a prolific blogger since 2005 and have authored 1500+ blogs on technology. Arun has extensive speaking experience in ~40 countries on myriad topics and is a JavaOne Rockstar.