How can I customize my deep learning environment at the notebook, cluster, and job levels in Databricks?
In Databricks, you can customize your deep learning environment at the notebook, cluster, and job levels as follows:

1. **Notebook level**: You can use notebook-scoped Python libraries or notebook-scoped R libraries to use a specific set or version of libraries without affecting other cluster users.

2. **Cluster level**: You can install libraries at the cluster level to standardize versions for a team or a project.

3. **Job level**: You can set up a Databricks job to ensure that a repeated task runs in a consistent, unchanging environment.

Additionally, you can create cluster policies to guide data scientists to the right choices, such as using a Single Node cluster for development and using an autoscaling cluster for large jobs.