🏗️Software Engineering: Continuous Integration & Continuous Deployment [CI/CD] (Just the Quotes)

"The deployment pipeline has its foundations in the process of continuous integration and is in essence the principle of continuous integration taken to its logical conclusion. The aim of the deployment pipeline is threefold. First, it makes every part of the process of building, deploying, testing, and releasing software visible to everybody involved, aiding collaboration. Second, it improves feedback so that problems are identified, and so resolved, as early in the process as possible. Finally, it enables teams to deploy and release any version of their software to any environment at will through a fully automated process." (David Farley & Jez Humble, "Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation", 2010)

"In essence, Continuous Integration is about reducing risk by providing faster feedback. First and foremost, it is designed to help identify and fix integration and regression issues faster, resulting in smoother, quicker delivery, and fewer bugs. By providing better visibility for both technical and non-technical team members on the state of the project, Continuous Integration can open and facilitate communication channels between team members and encourage collaborative problem solving and process improvement. And, by automating the deployment process, Continuous Integration helps you get your software into the hands of the testers and the end users faster, more reliably, and with less effort." (John F Smart, "Jenkins: The Definitive Guide", 2011)

"Remember, the most basic function of any Continuous Integration tool is to monitor source code in a version control system and to fetch and build the latest version of your source code whenever any changes are committed." (John F Smart, "Jenkins: The Definitive Guide", 2011)

"Continuous deployment is but one of many powerful tools at your disposal for increasing iteration speed. Other options include investing in time-saving tools, improving your debugging loops, mastering your programming workflows, and, more generally, removing any bottlenecks that you identify." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"Many problems stem from a premature attempt at scaling Agile within the organization. The nature of the transformation is such that it is unrealistic to plan upfront for an 18-month organization-wide change program to go from status quo to continuous delivery. People try nevertheless, and when the outcomes don’t materialize, they say Agile doesn’t work." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Why is continuous deployment such a powerful tool? Fundamentally, it allows engineers to make and deploy small, incremental changes rather than the larger, batched changes typical at other companies. That shift in approach eliminates a significant amount of overhead associated with traditional release processes, making it easier to reason about changes and enabling engineers to iterate much more quickly." (Edmond Lau, "The Effective Engineer: How to Leverage Your Efforts In Software Engineering to Make a Disproportionate and Meaningful Impact", 2015)

"[…] the practice of continuous integration helps a development team fail-fast in integrating code under development. A corollary of failing fast is to aim for fast feedback. The practice of regularly showcasing (demoing) features under development to product owners and business stakeholders helps them verify whether it is what they asked for and decide whether it is what they really want." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"DevOps and Continuous Integration/Continuous Deployment (CI/CD) are vital to any software project that is developed by more than one developer and needs to uphold quality standards. A central code repository that offers versioning, branching, and merging for collaborative development and approval workflows and documentation features is the minimum requirement here." (Patrik Borosch, "Cloud Scale Analytics with Azure Data Services: Build modern data warehouses on Microsoft Azure", 2021)

"[...] ML engineers tend to focus on MLOps and DevOps, which are tradition‐ally focused on deployment automation, infrastructure-as-code, and CI/CD. On theother hand, data scientists tend to focus on training and evaluating ML models. While the two worlds have collided, there remains a competency gap between ML engineers (automation) and d? ata scientists (model evaluation) in many teams. We know how to set up CI pipelines and we know how to train and evaluate models, but not all teams have worked out how to bridge both practices to automate manual model evaluation procedures." (David Tan et al,"Effective Machine Learning Teams: Best Practices for ML Practitioners", 2024)