Friday, 21 August 2020

Getting Started with Terraform for Azure in Windows 10

Terraform is a great way to setup infrastructure as code (IaC) for Azure. Terraform helps us to codify and version control our infrastructure needs in multiple platforms, hence, making learning terraform for IaC for Azure would let an individual to easily get adapted to other platforms such as AWS. IN this post let’s have a quick look at preparing a Windows 10 machine to get started with terraform.

Friday, 14 August 2020

Change Routing of Azure Function Apps

When you implement functions in Azure by default the routing is the https://functionappname/api/functionname . However, this implementation would not let you proper organizing of routing when you have multiple function apps in your software application project. You might want to create custom routing to make your function access from other application organized appropriately. Let’s look at default behaviors and how we can setup custom routing.

Friday, 7 August 2020

How to Run GitHub Actions Step When a Failure Occurred in a Previous Step

GitHub Actions are the CI/CD workflow implementation tool built into GitHub repos. While using the GitHub Actions workflows you may want to execute a cleanup, or rollback or even a ticket(issue) creation task in a situation where a job step is failed. In Azure DevOps pipelines each task had control support to easy implantations of the run on failure need. Let’s look at what it is in Azure DevOps then understand how we can achieve same goals in GitHub actions workflow steps.

Friday, 31 July 2020

Allow Azure Services on SQL Server with Azure CLI

Allow Azure services on Azure SQL Server lets other Azure Services such as function apps, app service apps etc. to be connected to an Azure SQL Server without needing to allow the outbound IPs of such services. You can enable this easily using the portal. Let’s look at how we can Allow Azure services suing CLI.

Monday, 27 July 2020

Git Repo Submodule Checkout in Azure DevOps Build Pipelines

Submodule in Git repos help you to keep the common code modules in a separate repo and utilize in multiple other repos. When you clone the git repo you can include submodules by using git clone --recurse-submodules. In Azure pipelines you can enable checking out the code with submodules for build and package purpose. Let’s have a look at the settings to enable submodule checkout in builds.

Wednesday, 8 July 2020

Cross Repo Branch Policies in Azure Git Repos

Azure Git repos provide protection to branches with branch policies. The cross-repo branch policy in a team project now lets you define policies applicable to a branch pattern, where it would even be applied to future branches which are adhering to the specified pattern. Let’s explore this feature in bit of detail.

Thursday, 18 June 2020

Azure Web App Creation with Azure CLI --runtime Specification Issues in PowerShell Scripts

PowerShell scripts and Azure CLI is a good combination to use for creating infrastructure as code targeting Azure platform. When creating an Azure app service app on Linux, you need to provide the --runtime argument specifying the web app runtime or the platform of the source code getting deployed. In a PowerShell window the command with --runtime argument fails, since a piping symbol is used in runtime arguments.

Saturday, 16 May 2020

Deploying Infrastructure to AWS LIghtsail Using Azure DevOps – Part 2 – Creating a Service Connection

In the previous post, we have discussed how to write a bash script with AWS CLI to create AWS Lightsail instance. In order to run this script to create AWS Lightsail instance via Azure DevOps we need to make a service connection to AWS from Azure DevOps. Let’s look at the steps to create such service connection.

Sunday, 10 May 2020

Deploying Infrastructure to AWS LIghtsail Using Azure DevOps – Part 1 - Writing IaC Script with bash

AWS Lightsail is easy to use cloud platform services by Amazon Web Services. You can use aws command line capabilities with Azure DevOps to deploy the required infrastructure on AWS Lightsail. Let’s look at step by step to see how we can get AWS Lightsail deployments via Azure DevOps.

Tuesday, 7 April 2020

Copy Azure Git Repo Branch Policies from One Branch to Another

Some teams when they practice Agile sprints keep a branch for the life of the sprint. In this case they do branch to develop features, make pull requests to sprint branch and release from the sprint branch at the end of the sprint. It is important to protect the sprint branch from incoming pull requests with policies. Since, there is no out of the box way to copy over the policies defined in one branch to another, each sprint the policies need to be created for the new sprint branch, which makes it a cumbersome process. In order to make it possible to copy set of policies defined in a branch to another, in Azure Git repos, now you can use the script made available here.

Monday, 3 February 2020

Pushing NuGet Packages to Azure DevOps Artifact Feeds Manually

In the build pipelines of Azure DevOps we can easily push a NuGet package, using a NuGet push step and selecting the artifact feed, in the Azure DevOps organization or team project. But you may sometimes need to push packages manually to Azure Artifact feeds. Let’s look at how we can do that.

Friday, 17 January 2020

Running EF Commands in Builds with .NET Core 3.1 in Hosted Agents

When you run builds with Entity Framework (EF) commands such as dotnet ef migrations script with .NET Core 2.2 it would work without any issue. However, if you upgrade your projects to use .NET Core 3.1 your build may fail with issue below., when executing dotnet ef migrations script, to generate a script out of your EF migrations.
error NETSDK1045: The current .NET SDK does not support targeting .NET Core 3.1. Either target .NET Core 2.2 or lower, or use a version of the .NET SDK that supports .NET Core 3.1.

Thursday, 9 January 2020

Deploying Machine Learning (ML) Model with Azure Pipeline Using Deployable Artifact from Build

We have discussed how to create a Machine Learning (ML) model as a deployable artifact in the post “Training Machine Learning (ML) Model with Azure Pipeline and Output ML Model as Deployable Artifact” which is based on the open source ML repo, (https://github.com/SaschaDittmann/MLOps-Lab.git) with data by Sascha Dittmann, which also contains the code to train a model.

Thursday, 2 January 2020

Training Machine Learning (ML) Model with Azure Pipeline and Output ML Model as Deployable Artifact

Training a machine learning model requires wide range of quality data to get the ML model trained in such a way that it can provide accurate predictions. Azure build pipeline can run the python tests written to validate the data quality and the train a model with uploaded data to Azure ML workspace. In this post on “Setup MLOPS workspace using Azure DevOps pipeline” it is clearly explained how to setup an Azure ML workspace in a new resource group dynamically with Azure CLI ML command extension. The post “Setup MLOPS workspace using Azure DevOps pipeline” as well as this post on training a model with Azure pipelines use the open source ML repo (https://github.com/SaschaDittmann/MLOps-Lab.git) with data by Sascha Dittmann, and code to train a model.

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