Azure DevOps Pipelines: Practices for Scaling Templates
When starting to leverage YAML Pipelines at scale you are most likely going to gravitate towards a solution that leverages a consolidated template repository. Since we will have multiple pipelines calling the same repository it's important we get guidance on how to start and structure this new repository, so it scales with our organization and their pipeline adoption.Azure DevOps Pipelines: Templates Tasks and Jobs
Fully leveraging the power of Azure DevOps (ADO) one should evaluate how to best use templates. This post focuses on using templates for tasks and jobs. Previous posts on Azure DevOps Pipelines: Environments and Variables (microsoft.com) and Azure DevOps Pipelines: Tasks, Jobs, Stages (microsoft.com)Getting secrets from Key Vault in YAML pipeline
If you have ever created an Azure App Service or Azure Function App that uses app settings, then you have dealt with the problem of how you are going to get those settings secure and updated correctly in each environment. You need a secure location to store this information and then be able to access it during your deployment process. Azure Key Vault and using the Azure Key Vault task inside a deployment pipeline in Azure DevOps can solve this problem for you. If you prefer video, then have a look at this as it will walk you through the steps of getting this setup.
Utilizing Azure Key vault with Private link in DevOps
Azure Key Vault is a cloud service that provides secure storage and access to secrets such as API keys, passwords, certificates, or cryptographic keys. To enhance security and disable public access, Azure Key Vault can be integrated with Private Endpoint powered by Azure Private Link. This private endpoint uses a private IP address from your VNet and brings the service into your VNet, effectively eliminating exposure from the public Internet by traversing traffic between your virtual network and the service over the Microsoft backbone network.
Modernize DevSecOps and GitOps journey with Microsoft’s Unified solution (Azure DevOps + GitHub)
Modernize your DevSecOps and GitOps journey with Microsoft’s Unified solution and best-in-class tools (Azure DevOps + GitHub) to Simplify, Automate, Secure entire software supply chain including containers, and Govern each phase with shift-left approach.Unlocking the Power of Open AI – Azure DevOps Backlogs from Images/PDFs
In today's digital world, the need to convert images and PDFs to text is becoming increasingly important. However, the process of manually transcribing images and PDFs can be time-consuming and error-prone. Fortunately, there is a better way. With the Azure Open AI service, you can easily and quickly convert images and PDFs to text.Azure DevOps Pipelines: Discovering the Ideal Service Connection Strategy
Embarking on the journey of deploying code into Azure through Azure DevOps brings forth a crucial question: how should your DevOps environment communicate with your Azure resources? In this in-depth exploration, we dive into the intricacies of configuring Service Connections and unravel the decision-making process surrounding their deployment.Azure OpenAI GPT model to review Pull Requests for Azure DevOps
In recent months, the use of Generative Pre-trained Transformer (GPT) models for natural language processing (NLP) has gained significant traction. GPT models, which are based on the Transformer architecture, can generate text from arbitrary sources of input data and can be trained to identify errors and detect anomalies in text. As such, GPT models are increasingly being used for a variety of applications, ranging from natural language understanding to text summarization and question-answering. In the software development world, developers use pull requests to submit proposed changes to a codebase. However, reviews by other developers can sometimes take a long time and not accurate, and in some cases, these reviews can introduce new bugs and issues. In order to reduce this risk, During my research I found the integration of GPT models is possible and we can add Azure OpenAI service as pull request reviewers for Azure Pipelines service. The GPT models are trained on developer codebases and are able to detect potential coding issues such as typos, syntax errors, style inconsistencies and code smells. In addition, they can also assess code structure and suggest improvements to the overall code quality. Once the GPT models have been trained, they can be integrated into the Azure Pipelines service so that they can automatically review pull requests and provide feedback. This helps to reduce the time taken for code reviews, as well as reduce the likelihood of introducing bugs and issues.
