You have an Azure Machine Learning workspace named "workspace1" containing a Python SDK v2 notebook that uses MLflow to log model training metrics and artifacts from your local computer. You now need to run this same notebook on an Azure Machine Learning compute instance within workspace1 while maintaining the ability to log metrics and artifacts from your data science code.

What should you do?