Making a custom virtual machine (VM) image in Microsoft Azure lets you standardize configurations and streamline deployment throughout your organization. Instead of repeatedly putting in software and making use of configurations on new VMs, you possibly can capture a base image once and use it as a template. This step-by-step guide walks through the process of building and capturing a custom Azure VM image.
Step 1: Plan the Customized Image
Before you start, define the purpose of your image. Consider what applications, settings, or configurations must be included. For example, you may want an image with preinstalled software packages, company security policies, or specific development tools. Proper planning ensures your image will be reusable and consistent throughout deployments.
Step 2: Create a Virtual Machine in Azure
Sign in to the Azure Portal.
Navigate to Virtual Machines and click + Create > Azure virtual machine.
Choose your subscription, resource group, and enter a VM name.
Choose the area, availability options, and an image to start with (reminiscent of Windows Server or Ubuntu).
Choose the appropriate VM size.
Configure administrator credentials.
Under Networking, set the virtual network, subnet, and public IP as needed.
Overview your settings and click Create to deploy the VM.
This VM will serve as the bottom machine where you put in your customizations.
Step 3: Join and Customise the VM
Once the VM is deployed:
For Windows: Use Remote Desktop (RDP) to connect.
For Linux: Use SSH.
Inside the VM, install all required software, apply system updates, configure firewalls, and set policies or scripts. The goal is to fully put together the environment precisely as you want future VMs to be provisioned.
For instance, chances are you’ll set up web servers, application frameworks, monitoring agents, or productivity tools. As soon as all modifications are complete, clean up pointless files and temporary data so the image remains efficient.
Step four: Generalize the VM
Before capturing an image, the VM have to be “generalized.” This process removes machine-specific information akin to pc name, security identifiers (SIDs), and log files, making certain that each VM created from the image is unique.
Windows VM:
Hook up with the VM.
Open a command prompt as Administrator.
Run:
sysprep.exe
Within the System Preparation Tool, choose Enter System Out-of-Box Expertise (OOBE) and check Generalize.
Set Shutdown because the motion and click OK.
The VM will shut down after Sysprep completes.
Linux VM:
Run the following command:
waagent -deprovision+consumer
Then shut down the VM.
At this point, the VM should not be restarted. Restarting it will break the generalization.
Step 5: Seize the VM as an Image
Within the Azure Portal, go to the VM you prepared.
Ensure the VM is in a Stopped (deallocated) state.
Select Seize from the toolbar.
Provide a name for the new image and select the resource group where it will be saved.
Optionally, check Automatically delete this virtual machine after creating the image to avoid unnecessary costs.
Click Review + Create.
Azure will create a new image resource that you need to use to deploy future VMs.
Step 6: Deploy New VMs from the Custom Image
To create a VM out of your custom image:
Go to Images within the Azure Portal.
Select your image.
Click + Create VM.
Configure the new VM settings as usual.
The VM will be deployed with all your preinstalled software and configurations, saving time and making certain consistency.
Benefits of Customized Images
Standardization: Ensures all VMs meet organizational requirements.
Effectivity: Reduces setup time for new machines.
Scalability: Makes it easy to duplicate environments across a number of areas or teams.
Security: Allows embedding compliance and security configurations directly into the bottom image.
By following these steps, you’ll be able to streamline your Azure environment with reusable, constant VM images tailored to your needs. Custom images are a strong tool for sustaining effectivity and uniformity throughout development, testing, and production workloads.
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