Inference Pipline Deployment Failing.

I am not expert in this matter but I will try to give you some hints that may help you to resolve your issue.

Even though the logs are long, they are the best source of information on what might be going wrong. Look for keywords like "error," "fail," or "exception" to identify specific issues.

# Check deployment logsaz ml model deploy --name mymodel --model model:1 --instance-type Standard_DS3_v2 --debug

Verify that your compute instance has enough resources (CPU, memory, ...) to handle the deployment.

Double-check that all necessary permissions are correctly configured:

• Blob Storage: Ensure that the service principal or identity being used has access to the blob storage.
• Compute Resources: Ensure that the deployment has the necessary permissions to create and manage compute resources.

Sometimes, issues can arise from the Docker image or environment setup:

• Base Image: Make sure you are using a compatible base image that supports the libraries and frameworks your model requires.
• Custom Dockerfile: If you're using a custom Dockerfile, verify that it is correctly set up and that all necessary libraries are installed.

Check if you are hitting any quota limits on your Azure subscription, such as the number of compute instances or concurrent deployments.

If the issue persists after troubleshooting, consider reaching out to Azure Support for detailed assistance.

Helo @Mustafa Mian

It seems that you are facing a common issue within Azure ML, a common challenge when deploying machine learning models as web services.

The error message you provided, "Deploy: Failed on Waiting real-time endpoint creation. Details: Failed Building the Environment," suggests that there might be a problem with the environment setup required for deployment.

I have faced similar issues and the problem can be :

1. Check the Environment Configuration: Ensure that the environment definition (e.g., the Docker image, Python version, libraries, etc.) matches what your model requires. If the environment configuration is not set up correctly, the deployment process can fail during the "Prepare image" step.
2. Custom Dependencies: If your model relies on specific Python libraries or other dependencies not included by default in Azure ML environments, ensure they are properly listed in the environment YAML file or are included in the conda dependencies.
3. Compute Resources: The compute instance you're using might not have enough resources (CPU, memory, or GPU) to handle the deployment process. Try using a more powerful compute instance or checking the resource quotas in your Azure subscription.
4. Quota Limits: Check your Azure subscription's quota limits for the specific region you're deploying in. If you exceed these quotas, deployments might fail.
5. Check the Environment Configuration: Ensure that the environment definition (e.g., the Docker image, Python version, libraries, etc.) matches what your model requires. If the environment configuration is not set up correctly, the deployment process can fail during the "Prepare image" step.
6. Custom Dependencies: If your model relies on specific Python libraries or other dependencies not included by default in Azure ML environments, ensure they are properly listed in the environment YAML file or are included in the conda dependencies.
7. Compute Resources: The compute instance you're using might not have enough resources (CPU, memory, or GPU) to handle the deployment process. Try using a more powerful compute instance or checking the resource quotas in your Azure subscription.
8. Quota Limits: Check your Azure subscription's quota limits for the specific region you're deploying in. If you exceed these quotas, deployments might fail.

Suggestions :

Verify and Update Environment Configuration:

 - Confirm that all dependencies are correctly listed and configured in your environment.

Review Detailed Logs:

• Examine the logs thoroughly, paying attention to any specific errors or issues encountered during the “Prepare image” step. Explore Different Compute Targets and Environment Configurations:
• Consider experimenting with alternative compute targets or adjusting environment settings. Sometimes changing these parameters can resolve deployment issues. If nothing bring ssoe progress kinldy try getting some detailed logs so we can have a look !

I hope this helps!

Kindly mark the answer as Accepted and Upvote in case it helped!

Regards