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I have the following deployment configuration for both local deployment (using a local work pool) and azure deployment (using a push pool).

```name: ml-ops prefect-version: 2.19.1 # build section allows you to manage and build docker images build: null # push section allows you to manage if and how this project is uploaded to remote locations push: null # pull section allows you to provide instructions for cloning this project in remote locations pull: - prefect.deployments.steps.git_clone: id: clone-step repository: XXXXXXX branch: retrain_pipeline credentials: "{{ prefect.blocks.github-credentials.github-access-token }}" - prefect.deployments.steps.run_shell_script: id: list-directory-structure script: "ls -R {{ clone-step.directory }}" stream_output: True - prefect.deployments.steps.pip_install_requirements: directory: "{{ clone-step.directory }}" requirements_file: prefect-requirements.txt stream_output: True - prefect.deployments.steps.set_working_directory: directory: "/opt/prefect/{{ clone-step.directory }}/code_base/models/performance_model/" # the deployments section allows you to provide configuration for deploying flows deployments: - name: my-local-deployment version: tags: [] description: entrypoint: prefect_flow.py:run_flow parameters: {} work_pool: name: my-d

@Marvin I have a deployment configuration for both local deployment (using a local work pool) and azure deployment (using a push pool). Except from the work pool the deployments are identical. My local deployment is working while my Azure deployment fails with a undescriptive MLFlow error:

mlflow.exceptions.ExecutionException: Run (ID '6af723af94ec483c8831c337b59a0e4c') failed

Do you what could cause a difference in local docker deployment and Azure Container Instance deployment? Or do you have a suggestion on how to debug this?

So, azure deployment issues can be notoriously difficult to pinpoint without more context. However, based on the documentation from Prefect and related material, I have a few suggestions to guide your debugging process: 1. Permissions: Ensure that the service account used in Azure has the same permissions as your local deployment for necessary resources like storage, logging, and networking. 2. Networking Differences: Check if Azure has any networking policies or security groups that might prevent communication between services. 3. Environment Variables and Configuration: Ensure all necessary environment variables and configurations for MLFlow are correctly set and aligned between local and Azure environments. 4. Logging and Debugging: - Increase the verbosity of logs in both Prefect and MLFlow to get more descriptive error messages. - Consider using Prefect's built-in logging and monitoring tools to gather more detailed information about where and why the error is occurring. ### Links to Docs for Reference: - Prefect Kubernetes Deployment Guide - Prefect Azure Container Instance Deployment - Prefect Block-Based Deployments No relevant GitHub issues were found regarding this specific error, so these debugging steps would be your best bet. Let's hope these steps provide some clarity. If you have more detailed logs or error messages, feel free to share, and we can dive deeper. Good luck.