Andreas Rehn is an Enterprise Architect and a strong advocate for Continuous Delivery, DevOps, Agile and Lean methods in systems development. 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 levels are not strict and mandatory stages that needs to be passed in sequence, but rather should serve as a base for evaluation and planning.
While they can serve as a starting point, they should not be considered as essential models to adopt and follow. 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 ci/cd maturity model 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. We specifically omit certain items such as microservices since you can achieve CD without using microservices.
If you manage many ML pipelines in production, you need
a CI/CD setup to automate the build, test, and deployment of ML pipelines. MLOps level 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. 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. At expert level some organizations choose to make a bigger effort and form complete cross functional teams that can be completely autonomous.
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). It was created in light of recent trends and best practices in software development, such as cloud native and DevOps. And with provisioning policy and code logging, organizations can view the entire deployment’s compliance rules, management, and audit logs. Let’s review the levels of infrastructure automation and how it has evolved over time. The result is the first pass at an evolving Infrastructure as Code Maturity Model. This model may be applied alongside the broader CD Maturity Model, or independently, to evaluate and further develop an organization’s infrastructure practices.
The following sections describe three levels of MLOps, starting
from the most common level, which involves no automation, up to automating both
ML and CI/CD pipelines. 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.
- You will likely find that automation is typically quite siloed and teams have differing approaches.
Automatically testing newly developed features to avoid tedious work.
- The idea is to keep your model crisp and to the point so that stakeholder don’t invest their time in looking something which they know is already working well or not required.
- The pros and cons of the continuous delivery maturity model will help the company decide whether its implementation is the right step at this time.
Once you identify a cause, try to reproduce the failure by rerunning the test or just manually reproducing it with the application under test. 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. The engineering team might have their own complex setup for API configuration,
testing, and deployment, including security, regression, and load and canary
testing. 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. To develop and operate complex systems like these, you can apply DevOps
principles to ML systems (MLOps).
– Trending improves the scrum team’s understanding of how done
The list is quite intimidating so we’ve highlighted the practices we think you should focus on when starting on this journey. The high priority practices were chosen because they give the most impact in terms of productivity, quality, delivery and risk mitigation. Make sure your system automatically alerts the right people when builds fail to ensure minimal downtime. This section discusses the components that you need to add to the architecture
to enable ML continuous training. At this stage in the model, the participants might be in a DevOps team, or simply developers and IT operations collaborating on a joint project. If your company is in a domain which is fairly stable or market doesn’t demand frequent delivery of features then “Zero touch deployment” may not be required.
Lastly, you’ll need a window into what level of infrastructure automation, ci cd maturity model if any, is already in use today. How is the success of this automation measured, and what is the overall outcome? You will likely find that automation is typically quite siloed and teams have differing approaches. DevOps is still a wild west in terms of how people approach it, said Williams. The level of automation of these steps defines the maturity of the ML process,
which reflects the velocity of training new models given new data or training
new models given new
Needed unification of build systems across scrum teams
Software-defined networking is based on a single physically centralized controller design with a static architecture based on unchangeable links and controller positions logically centralized with either a flat or hierarchical architecture. The organization’s network infrastructure using micro virtual segmentation in the campus infrastructure is based on a virtual extensible local area network. There is on-premise, enterprise-wide hybrid cloud application and infrastructure automation that is API driven using an automation tool on virtualized and non-virtualized platforms . Zero trust is an approach where access to data, networks and infrastructure is kept to what is minimally required and the legitimacy of that access must be continuously verified. In all four stages of maturity , CISA has also added several new functions and updated existing functions to consider when organizations plan and make decisions for zero trust architecture implementation.
At this level real time graphs and other reports will typically also include trends over time. The result is a system that is totally reproducible from source control, from the O/S and all the way up to application. Doing this enables you to reduce a lot of complexity and cost in other tools and techniques for e.g. disaster recovery that serves to ensure that the production environment is reproducible.
Building, Deploying, Testing, and Reporting
The CD3M maturity model has five levels from Foundation level (1) towards Expert level (5). In each maturity level a number of practices needs to be implemented to advance the CD 3.0 pipeline. The continuous delivery maturity model https://www.globalcloudteam.com/ has five steps (level) – base, beginner, intermediate, advanced, and expert. There are also five categories–Culture and Organization, Design and Architecture, Build and Deploy, Test and Verification, Information and Reporting.
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. Testing is without doubt very important for any software development operation and is an absolutely crucial part of a successful implementation of Continuous Delivery.
Needed regular builds of many build configurations
Recognizing that there were opportunities to optimize the pipeline for higher productivity, we began our journey toward continuous deployment. The following section discusses the typical steps for training and evaluating
an ML model to serve as a prediction service. An ML system is a software system, so similar practices apply to help guarantee
that you can reliably build and operate ML systems at scale.