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# ML Pipelines
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Workflow orchestration, scheduling, and complex ML pipeline management with Kubeflow Pipelines integration.
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## Capabilities
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### Pipeline Jobs
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Execute and manage ML workflows with comprehensive monitoring and scheduling.
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```python { .api }
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class PipelineJob:
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    @classmethod
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    def create(
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        cls,
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        display_name: str,
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        template_path: str,
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        job_id: Optional[str] = None,
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        pipeline_root: Optional[str] = None,
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        parameter_values: Optional[Dict[str, Any]] = None,
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        input_artifacts: Optional[Dict[str, str]] = None,
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        enable_caching: Optional[bool] = None,
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        encryption_spec_key_name: Optional[str] = None,
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        labels: Optional[Dict[str, str]] = None,
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        credentials: Optional[auth_credentials.Credentials] = None,
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        project: Optional[str] = None,
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        location: Optional[str] = None,
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        **kwargs
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    ) -> 'PipelineJob': ...
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    def run(
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        self,
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        service_account: Optional[str] = None,
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        network: Optional[str] = None,
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        sync: bool = True,
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        create_request_timeout: Optional[float] = None,
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        **kwargs
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    ) -> None: ...
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    @property
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    def state(self) -> PipelineState: ...
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    @property
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    def task_details(self) -> List[PipelineTaskDetail]: ...
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```
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### Pipeline Scheduling
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Schedule pipeline executions with cron expressions and comprehensive scheduling options.
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```python { .api }
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class PipelineJobSchedule:
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    @classmethod
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    def create(
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        cls,
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        pipeline_job: PipelineJob,
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        display_name: str,
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        cron: str,
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        max_concurrent_run_count: int = 1,
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        max_run_count: Optional[int] = None,
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        service_account: Optional[str] = None,
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        network: Optional[str] = None,
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        labels: Optional[Dict[str, str]] = None,
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        **kwargs
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    ) -> 'PipelineJobSchedule': ...
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    def pause(self) -> None: ...
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    def resume(self) -> None: ...
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    def update(self, **kwargs) -> 'PipelineJobSchedule': ...
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```