---
title: Scheduling and Triggering Task Execution
---

 In ClearML, tasks can be scheduled and triggered automatically, enabling seamless workflow automation. This section 
 provides an overview of the mechanisms available for managing task scheduling and event-based 
 triggering.

## Task Scheduling 
Task scheduling allows users to define one-shot or periodic executions at specified times and intervals. This 
is useful for:

* Running routine operations such as periodic model training, evaluation jobs, backups, and reports.
* Automating data ingestion and preprocessing workflows.
* Ensuring regular execution of monitoring and reporting tasks.

ClearML's offers the following scheduling solutions: 
* [**UI Application**](../webapp/applications/apps_task_scheduler.md) (available under the Enterprise Plan) - The **Task Scheduler** app
  provides a simple no-code interface for managing task schedules. 

* [**Python Interface**](../references/sdk/scheduler.md) - Use the `TaskScheduler` class to programmatically manage
  task schedules. 
  
## Task Execution Triggering  

ClearML's trigger manager enables you to automate task execution based on event occurence in the ClearML system, such as:
* Changes in task status (e.g. running, completed, etc.)
* Publication, archiving, or tagging of tasks, models, or datasets
* Task metrics crossing predefined thresholds

This is useful for:
* Triggering a training task when a dataset has been tagged as `latest` or any other tag
* Running an inference task when a model has been published 
* Retraining a model when accuracy falls below a certain threshold
* And more 

ClearML's offers the following trigger management solutions: 
* [**UI Application**](../webapp/applications/apps_trigger_manager.md) (available under the Enterprise Plan) - The **Trigger Manager** app
  provides a simple no-code interface for managing task triggers .
* [**Python Interface**](../references/sdk/trigger.md) - Use the `TriggerScheduler` class to programmatically manage
  task triggers.