> For the complete documentation index, see [llms.txt](https://doc.verteego.com/verteego-doc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.verteego.com/verteego-doc/apps/recipes/pipelines.md).

# Pipelines

## **Forecast Pipelines**

When launching a Forecast Pipeline within a recipe, specific details need to be provided to ensure the correct execution of the predictive model:

* **pipeline\_name**: Identifies the particular pipeline configuration to be used. Mandatory.
* **train\_set**: Specifies the dataset for model training. Mandatory.
* **predict\_set**: Indicates the dataset on which predictions will be made. Mandatory.

#### Example

```yaml
launch_forecast_pipeline:
  type: forecast_pipeline
  params:
    pipeline_name: sales_forecast
    train_set: train_dataset
    predict_set: test_dataset
```

## **Optimization Pipelines**

Initiating an Optimization Pipeline requires identifying the dataset that comprises the optimization scenarios:

* **pipeline\_name**: The unique identifier for the optimization configuration. Mandatory.
* **optimization\_set**: The dataset containing the various options or scenarios for optimization. Mandatory.

**Example**

```yaml
launch_optimization_pipeline:
  type: optimization_pipeline
  params:
    pipeline_name: Fleet_Optimization
    optimization_set: fleet_planning_system
```

### Extract Pipeline Results

Different steps within the pipelines can be extracted as datasets using the `import_from_pipeline` method, providing flexibility in the output that you wish to analyze further.\
\
Learn more on [extracting pipeline results](/verteego-doc/apps/recipes/datasets.md#import-from-pipeline).