To schedule a pipeline in Azure Machine Learning SDK v2, you must instantiate three key classes in a logical dependency order. First, you must instantiate a PipelineJob (typically by calling a pipeline component function) to define the specific workload or training tasks to be executed. Second, you must instantiate a RecurrenceTrigger to define the timing. The question explicitly requires using "frequency" and "interval" properties (e.g., frequency='month', interval=1), which are properties of RecurrenceTrigger, whereas CronTrigger relies on cron expressions. Finally, you must instantiate the JobSchedule class, which serves as the container that binds the workload (create_job=pipeline_job) and the schedule configuration (trigger=recurrence_trigger) together to activate the automated process.

Microsoft Learn - Azure Machine Learning SDK v2 (Python): Documentation for the JobSchedule class, which confirms the requirement of trigger and create_job parameters for instantiation.

Source: JobSchedule Class - Azure Machine Learning Python SDK v2

Microsoft Learn - Schedule a job: Detailed guide on creating schedules, demonstrating the pattern of defining the pipeline job, creating a RecurrenceTrigger, and passing both to the JobSchedule constructor.

Source: Schedule a job with Azure Machine Learning

Microsoft Learn - RecurrenceTrigger Class: Verifies that this class uses frequency and interval properties, distinguishing it from CronTrigger.

Source: RecurrenceTrigger Class - Azure Machine Learning Python SDK v2