Environments are the way to incorporate third party libraries with serverless compute.

In the provided example, the environment has been correctly defined, but it needs to be linked to the job task. You can do this by adding an environment key in the task definition like this

# A serverless job (environment spec)
resources:
  jobs:
    serverless_job_environment:
      name: serverless_job_environment

      tasks:
        - task_key: task
          spark_python_task:
            python_file: ../src/main.py

          # The key that references an environment spec in a job.
          # https://docs.databricks.com/api/workspace/jobs/create#tasks-environment_key
          environment_key: default

      # A list of task execution environment specifications that can be referenced by tasks of this job.
      environments:
        - environment_key: default

          # Full documentation of this spec can be found at:
          # https://docs.databricks.com/api/workspace/jobs/create#environments-spec
          spec:
            client: '1'
            dependencies:
              - my-library