How to Make Continuous Delivery a Reality in an Agile Environment

Continuous integration and continuous delivery (CI/CD) are core elements of successful DevOps. Systems engineers may start with CI because it is familiar. With a DevOps focus, organizations uncover configuration, packaging, and orchestration that are necessary to effective software development life cycle (SDLC). This empowers developers, administrators and engineers to create valuable CD practices, adding to agility.

Where less experienced developers might struggle with CI/CD performance, testing delays and other bottlenecks, the enterprise would do well to develop processes and best practices to make DevOps in the cloud a value-driven methodology. To save money, this will shorten the SDLC — because CD is all about updating web services. In public clouds such as AWS and Azure, this is done through pipeline stages (e.g. dev, test, staging and production). When containers are implemented with a platform-as-a-service (PAAS) approach, stages become sandbox environments, scratch instances, and production instances.

The benefit of such an approach is that the work outputs and products themselves benefit from flexibility. Regular face-to-face interactions and collaborations between team members are conducted to ensure the scrum teams level-set expectations. Finally, add value by continuous delivery throughout the life cycle, so that the end product is more secure and more reliable. Implementing an agile manifesto tracks with addressing evolving end user requirements. 

For CD, ensure user stories are married correctly to those requirements and that each story rolls up to an Epic that represents a standalone feature. This enables the devops team to release reasonably sized components of functionality that are consumable by users. These are also traceable back to the release plan. We want to ensure verification at each stage because this process defines acceptance criteria — so the stakeholders know when something is declared “finished.”

Schema, user interface, access control rights and static resources such as icons and images are all part of the creation process and we manage them just as diligently as source code. The DevOps team checks assets into a version control system as a single source of truth (GIT or Subversion). This benefits the client by ensuring that developers are making changes in a segregated environment — catastrophic failures are completely avoided with such approach, and integration into a risk management-based security framework is seamless. 

The organization should understand automated quality processes are essential — Selenium is a go-to tool for testing functionality. There are several verifications to make before functional testing. Static code analysis tools, such as PMD, are essential to ensure code conforms to a single style. Unit test coverage is also essential — establish a set of Key Performance Inidcators (KPIs) for coverage of at least 75% of code. Finally, after these automated tests pass, implement a manual peer review. This enables seasoned developers  to spot opportunities for performance improvement where automated tools can’t.

Knowledge Worker Productivity Improvements with Machine Learning

Leveraging machine learning to enhance capabilities that can recognize context, concepts, and meaning means there are interesting new opportunities for collaboration between knowledge workers and computational power. For example, Bluedog’s experts can now provide more of their own input for training, quality control, and fine-tuning of algorithm-based outcomes. We use the computational power of our servers to augment the expertise of human collaborators — this helps to create new areas for our experts to leverage.

For example, at Bluedog, we utilize several algorithm-based tools to help us quickly assess opportunities for our clients. We extract information from Word Documents locally for multiple uses. With one tool, we take advantage of each Word document’s XML metadata. From there, we use a regex library to find each targeted word or phrase in the document, then adding them to a list. Our toll then performs for-loops to scan for relevant patterns in the XML to extract data.

Knowledge workers — the staff or consultants who reason, create, decide, and apply insight into non-routine cognitive processes — can contribute to redesigning work process roles and team member roles. Consider financial auditing, where AI is likely to become pervasive. Often, when AI offers a finding, the algorithm’s reasoning isn’t obvious to the accountant, who ultimately must offer an explanation to a client — characteristic of the “black box” problem. To improve this outcome, Bluedog recommends providing an interface so experts to enter concepts they deem important into the system and be provided with a means to test their own hypotheses. In this way, we recommend making models accessible to common sense. 

As cybersecurity concerns mount, organizations have increased the use of instruments to collect data at various points in their network to analyze threats — and to address “Internet-of-Things” (IoT) devices. However, many of these data-driven systems do not integrate data from multiple sources. Nor do they incorporate the common-sense knowledge of cybersecurity experts, who know the range and diverse motives of attackers, understand typical internal and external threats, and appreciate the degree of risk to an organization. 

Bluedog’s experts specify the use of Bayesian models — which employ probabilistic analysis to capture complex interdependence among risk factors —  combined with expert systems judgment. In cybersecurity for enterprise networks, complex factors may include large numbers and types of devices on the network. It is crucial to access the knowledge of the organization’s security experts about attackers and risk profile to better intercept cybercriminals.