Many times applications are developed in silos to cater to an enterprise’s immediate needs. While this may benefit a single application, it can cause multiple business units to create and maintain different applications, sometimes duplicating efforts. In other cases, an application may become so critical to the business that it goes untouched for years, causing the frameworks and underlying software to become outdated and slow. As a company’s IT becomes more and more legacy, making changes becomes costly, and moving to a microservices architecture becomes all the more difficult. Adopting an Azure DevOps approach can overcome these hurdles while helping the enterprise create an evergreen IT estate.


DevOps adoption can automate the CI/CD (Continuous Integration & Continuous Deployment) process, with Azure DevOps acting as a robust engine to drive this transformation. In Continuous Integration, the development team regularly updates the code changes in the repository, after which the automated builds and tests can then run. Meanwhile, Continuous Delivery helps organizations increase the number of releases, reduce manual work, and minimize the risk of failure during production, among other benefits. This results in Continuous Deployment, a situation in which the deployment process contains sub-processes (e.g. code creation, testing, versioning, deployment, post-deployment, etc.) with their code automatically deployed in a production environment after successfully passing all the test cases.


During this process, companies need to monitor several key metrics:

  • Deployment frequency
  • Failed deployments
  • Code committed
  • Lead Time
  • Error Rate
  • Mean time to detection
  • Mean time to recovery


Embracing an Azure DevOps approach can assist with DevOps adoption while tracking the elements above. Azure DevOps involves several core functions that enable the processes for enabling continuous integration and continuous deployment.

  • Azure Board – Azure boards helps to trace work with Kanban boards, backlogs, and custom defined details.
  • Azure Repo – Azure Repos provides unlimited Git repositories. We can create a project in Azure DevOps and create several repositories.
  • Azure Pipeline – Azure pipelines permits to make, test, and deploy with CI/CD that works with any language, platform, and cloud.
  • Azure Test Plan – Azure Test Plans (or check plans) help run test cases for applications and log defects. They help to track and assess quality throughout testing lifecycle.
  • Azure Artifacts – Azure Artifacts helps to make and share maven, NPM, and NuGet package feeds from public and personal sources. It is totally integrated into CI/CD pipelines.


Additionally, modernization based on artificial intelligence (AI) and machine learning (ML) can be enabled during the continuous integration cyle to keep the estate evergreen. A typical automated continuous-integration cycle can include auto remediation after the developer commits the code. This helps detect older code versions and remediate them before any code is pushed for deployment. Adding AI and ML to the process also makes the process more efficient. Here’s how it works:

After the developer commits code, the developer is asked if he/she wants to update the version to the latest version if the version is old. The code is analysed using tools like SonarQube, while the system auto-remediates the code as per certain preconfigured rules. The system then learns the code while it applies the preconfigured rules, checking whether the code follows best practices for logging, monitoring, exception handling, security, etc., and remediating it as required by adding/updating the appropriate components. The goal is to make the code compliant to 12-factor microservices.


AI can be leveraged to improve efficiency using bots, self-learning systems, and predictive systems that can better track production performance and establish links to past issues. Wipro’s devNXT platform, for example, has significant automation and AI/ML capabilities built into the Azure pipeline, helping accelerate clients’ Azure DevOps journeys and reducing their technical debt. For instance, we helped a leading US-based online auto-insurance provider adopt DevOps development to successfully modernize its applications.


Continuous processes, when assisted by bots and self-learning, help to ensure the code meets modern best practices and is regularly updated to the latest version, thus reducing technical debt. AI also helps to predict errors based on previous data, to identify root causes of key issues, to find anomalies in the threats recorded in central logging systems, and to find false alerts. In addition, AI-powered bots can improve collaboration between development and operation teams, with ML-based systems prioritizing responses that require management.


By adopting an Azure DevOps approach, companies can overcome technical hurdles, reduce their technical debt and improve efficiencies. Embracing continuous modernization also helps organizations build a foundation for the future, paving the way to realize scalable growth with an evergreen IT estate.

Rekha Kodali

Rekha Kodali

Solution Design and Strategy Head, Microsoft Practice


Rekha is a DMTS Distinguished member with more than 22 years of experience in Microsoft Technologies. Her focus areas include Azure, AI, Modern Apps, Micros services, API Management, BizTalk Server, Web Services, SharePoint, Office 365, SQL Server, ESB and SOA.

Leeladhar Ladia

Leeladhar Ladia



Leeladhar has 15+ years of experience in Microsoft technologies. He is TOGAF and MS Azure (Solution Architect) certified. A full stack developer and keen learner, he is always interested in new technologies, automation and innovative ideas.

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