Hi @Kabi, as of my knowledge databricks doesn’t support directly connecting to Databricks kernel. However, here are practical ways to sync your local notebook with Databricks:

• You can use Git to version control your notebooks. Clone your repo into Databricks via Repos, edit locally or in Databricks, and push changes to keep both environments in sync.
• Install databricks-connect to execute local code on a Databricks cluster. Ensure your local Python version matches the cluster’s runtime.
• Use the Databricks VSCode extension to sync files between your local machine and Databricks workspace. It's easy to develop locally while using Databricks clusters for execution.

And as you mentioned, for MLflow, set up your DATABRICKS_TOKEN to securely connect to tracking servers hosted on Databricks.

From my experience, first two ways are simplest way to sync notebooks.