No. Deleting an existing Azure Machine Learning kernel is neither required nor appropriate before adding another Jupyter kernel. Azure Machine Learning compute instances are explicitly designed to support multiple kernels simultaneously . Microsoft states that notebooks automatically discover the Jupyter kernels installed on the connected compute instance, allowing users to switch between them from the kernel selector.

To add a kernel, the supported process is to create a new Conda environment, activate it, install pip and ipykernel, and then register that environment by using python -m ipykernel install --user. Existing kernels do not need to be removed.

More importantly, Microsoft warns against deleting Conda environments or Jupyter kernels that you did not create , because doing so can damage Jupyter or JupyterLab functionality on the Azure Machine Learning compute instance. This makes removal of a built-in AzureML kernel particularly inappropriate as a prerequisite for creating another kernel.

The correct approach is therefore to leave the existing kernel intact and create a separate environment/kernel for the new workload.

Study Guide Reference: Design and implement an MLOps infrastructure — Azure Machine Learning notebook environments, kernel management, compute instances, and dependency isolation.

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