CI CD A Guide to Maturity Continuous Integration is a development by Ryan Krull Standard Bank Engineering

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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. JCGs is an independent online community focused on creating the ultimate Java to Java developers resource center; targeted at the technical architect, technical team lead , project manager and junior developers alike. The bedrock of DevOps, the continuous improvement mindset, is so ingrained that teams can accurately describe how they’re improving. It’s not just that, either; they can say by how much and over what time windows. The product team makes decisions about what features to prioritize based on hard data and conversations with key customers.

Wants to change the way we look at systems development today, moving it to the next level where we focus more time on developing features than doing manually repetitive tasks. Where we visualize and understand the path from idea to where it is released and brings business value. 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.

Why a maturity model?

For any non-trivial business of reasonable size this will unfortunately include quite a lot of steps and activities. These are questions that inevitably will come up when you start looking at implementing Continuous Delivery. Continuous Delivery Maturity Models provide frameworks for assessing your progress towards adopting and implementing continuous integration, delivery and deployment (CI/CD). The maturity model includes 5 levels each one covering people, process, policy and technology. Manual deployment to a production environment after several successful runs of the pipeline on the pre-production environment. 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.

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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. Instead of having a separate process, disaster recovery is simply done by pushing out the last release from the pipeline like any other release. This together with virtualization gives extreme flexibility in setting up test and production environments with minimum manual effort. We specifically omit certain items such as microservices since you can achieve CD without using microservices.

At this advanced level, teams also tackle harder deployment problems, such as multi-tier applications in which several components must deploy together, but are on different release cycles. Continuous Integration integrates the new/changed code into the current system after each check-in without any manual steps. This can be realized by using a workflow orchestrator such as Jenkins or VSTS where you can configure a pipeline to do that for you. Best practices for Continuous Integration are having a build that can be used for all environments and using a microservice architecture. In the most ideal situation you want to automatically up- and down-scale the continuous integration services based on how much you are using them. These tests are especially valuable when working in a highly component based architecture or when good complete integration tests are difficult to implement or too slow to run frequently.

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There are many open source and commercial tool offerings, each claiming to simplify the development team’s work while increasing confidence in the published artifacts. Project Managers need to weigh in the needs of the company against the various feature sets of these tools. Many commercial tools strive are kitchen sink solutions targeting large scale enterprise development. Often times these solutions create complications and bottlenecks for small projects that do not need to collaborate with 5000 developers and multiple product lines, or multiple versions.

You can also use continuous feedback from production to inform hypothesis-driven development . However, you need to try new ML ideas and rapidly deploy new implementations of the ML components. If you manage many ML pipelines in production, you need a CI/CD setup to automate the build, test, and deployment of ML pipelines.

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DevOps

The goal of CI/CD is to deliver better quality software by preventing issues before they occur by testing earlier. This comes from the ability to identify defects and quality issues on smaller changes in code, earlier in the process. 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. As you continue to build out the pipeline, your team will need to collaborate more closely with other functions and start taking more responsibility for delivering your software.

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. At a base level you will have a code base that is version controlled and scripted builds are run regularly on a dedicated build server.

Enterprise Agility

At intermediate level, builds are typically triggered from the source control system on each commit, tying a specific commit to a specific build. Tagging and versioning of builds is automated and the deployment process is standardized over all environments. Built artifacts or release packages are built only once and are designed to be able to be deployed in any environment. The standardized deployment process will also include a base for automated database deploys of the bulk of database changes, and scripted runtime configuration changes. A basic delivery pipeline is in place covering all the stages from source control to production.

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While agile methodologies often are described to best grow from inside the organization we have found that this approach also has limitations. Some parts of the organization are not mature enough to adapt and consequently inhibit development, creating organizational boundaries that can be very hard to break down. The best way to include the whole organization in the change is to establish a solid platform with some important prerequisites that will enable the organization to evolve in the right direction. 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. Assuming that new implementations of the pipeline aren’t frequently deployed and you are managing only a few pipelines, you usually manually test the pipeline and its components. You also submit the tested source code for the pipeline to the IT team to deploy to the target environment.

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• Making sure that the performance of the model is consistent on various segments of the data.
• While cloud native is not a linear evolution, and setbacks will be experienced, the first level in the cloud native maturity is to understand how you will measure success and how you’ll demonstrate those to stakeholders.
• Expert practices will include zero touch continuous deployment to production where every commit can potentially make it all the way to production automatically.
• Continuous Delivery Maturity Models provide frameworks for assessing your progress towards adopting and implementing continuous integration, delivery and deployment (CI/CD).
• I like the idea a lot and would like to use that model for us to evaluate our own maturity.

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. Hold the build in a staging environment while completing manual regression testing, all stakeholders to complete UAT, and deployment to production approval. At Devbridge, we recognized the value of complete deployment automation and resolved to include continuous deployment as part of our processes and best practices.

From new ways of working to deeply technical tools-based topics, you can

GitOps has emerged as a key technology in the cloud native computing space over the last few years. Research into delivery velocity has shown that speeding up software delivery is closely correlated with business success. GitOps is an approach for building incredibly robust and repeatable continuous delivery pipelines.

Manual regression testing took an entire day to complete, with the team wasting valuable time waiting for results. Several Continuous Delivery Maturity Models are available, such as InfoQ, UrbanCode, ThoughtWorks, Bekk, and others. A detailed explanation of what each level of GitOps maturity looks like in practice. Building up your pipeline incrementally, with achievable goals along the way, makes the process more manageable and provides opportunities to take stock and learn from what you have done so far.

Additional components

We believe there are four fundamental areas that organizations should focus on when adopting DevOps. The suggested tools are the tools we have experience with at Standard Bank. The tools listed aren’t necessarily the best available nor the most suitable ci cd maturity model for your specific needs. You still need to do the necessary due diligence to ensure you pick the best tools for your environment. What tools did you have in mind to “[…] provide dynamic self-service useful information and customized dashboards.”

Advancing from continuous delivery to continuous deployment

At beginner level, the monolithic structure of the system is addressed by splitting the system into modules. Modules give a better structure for development, build and deployment but are typically not individually releasable like components. Doing this will also naturally drive an API managed approach to describe internal dependencies and also influence applying a structured approach to manage 3rd party libraries. At this level the importance of applying version control to database changes will also reveal itself.

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. To address the challenges of this manual process, MLOps practices for CI/CD and CT are helpful. By deploying an ML training pipeline, you can enable CT, and you can set up a CI/CD system to rapidly test, build, and deploy new implementations of the ML pipeline. 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.