diff --git a/docs/guides/pipeline/pipeline_controller.md b/docs/guides/pipeline/pipeline_controller.md
index 839c7d70..e00a4de3 100644
--- a/docs/guides/pipeline/pipeline_controller.md
+++ b/docs/guides/pipeline/pipeline_controller.md
@@ -1,46 +1,36 @@
 ---
-title: Simple Pipeline - Serialized Data
+title: Pipeline from Tasks
 ---
 
 The [pipeline_from_tasks.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_tasks.py) 
-example demonstrates a simple pipeline in **ClearML**. 
-This pipeline is composed of three steps: 
-1. Download data
-1. Process data
-3. Train a network. 
-   
-It is implemented using the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md) 
-class. This class includes functionality to: 
-* Create a pipeline controller
-* Add steps to the pipeline 
-* Pass data from one step to another
-* Control the dependencies of a step beginning only after other steps complete
-* Add callback functions to be executed pre- and post-step execution  
-* Run the pipeline 
-* Wait for the pipeline to complete 
-* Cleanup after pipeline completes execution
+example demonstrates a simple pipeline, where each step is a [ClearML Task](../../fundamentals/task.md). 
+
+The pipeline is implemented using the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md) 
+class. Steps are added to a PipelineController object, which launches and monitors the steps when executed.
  
-This example implements the pipeline with four Tasks (each Task is created using a different script):
+This example incorporates four tasks, each of which is created using a different script:
 * **Controller Task** ([pipeline_from_tasks.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_tasks.py)) - 
-  Creates a pipeline controller, adds the steps (Tasks) to the pipeline, runs the pipeline. 
-* **Step 1 Task** ([step1_dataset_artifact.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step1_dataset_artifact.py)) - 
+  Implements the pipeline controller, adds the steps (tasks) to the pipeline, and runs the pipeline.
+* **Step 1** ([step1_dataset_artifact.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step1_dataset_artifact.py)) - 
   Downloads data and stores the data as an artifact.
-* **Step 2 Task** ([step2_data_processing.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step2_data_processing.py)) - 
+* **Step 2** ([step2_data_processing.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step2_data_processing.py)) - 
   Loads the stored data (from Step 1), processes it, and stores the processed data as artifacts.
-* **Step 3 Task** ([step3_train_model.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step3_train_model.py)) - 
+* **Step 3** ([step3_train_model.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step3_train_model.py)) - 
   Loads the processed data (from Step 2) and trains a network.
 
-When the pipeline runs, the Step 1, Step 2, and Step 3 Tasks are cloned, and the newly cloned Tasks are executed. The Tasks 
-they are cloned from, called the base Tasks, do not execute. This way, the pipeline can run multiple times. These 
-base Tasks must have already run at least once for them to be in **ClearML Server** and to be cloned. The controller Task 
-itself can be run from a development environment (by running the script), or cloned, and the cloned Task executed remotely (if the 
-controller Task has already run at least once and is in **ClearML Server**).
+When the controller task is executed, it clones the step tasks, and enqueues the newly cloned tasks for execution. Note 
+that the base tasks from which the steps are cloned are only used as templates and not executed themselves. Also note 
+that for the controller to clone, these base tasks need to exist in the system (as a result of a previous run or using 
+[clearml-task](../../apps/clearml_task.md)).
 
-The sections below describe in more detail what happens in the controller Task and in each step Task.
+The controller task itself can be run locally, or, if the controller task has already run at least once and is in the 
+ClearML Server, the controller can be cloned, and the cloned task can be executed remotely.
+
+The sections below describe in more detail what happens in the controller task and in each step task.
 
 ## The Pipeline Controller
 
-1. Create the pipeline controller object.
+1. Create the [pipeline controller](../../references/sdk/automation_controller_pipelinecontroller.md) object.
 
    ```python
    pipe = PipelineController(
@@ -56,7 +46,7 @@ The sections below describe in more detail what happens in the controller Task a
    automatically bumped if the same version already exists. 
    * `add_pipeline_tags` - If `True`, then all pipeline steps are tagged with `pipe: <pipeline_task_id>` 
  
-1. Add Step 1. Call the [PipelineController.add_step](../../references/sdk/automation_controller_pipelinecontroller.md#add_step) 
+1. Add Step 1 using the [PipelineController.add_step](../../references/sdk/automation_controller_pipelinecontroller.md#add_step) 
    method.
    
    ```python
@@ -64,7 +54,7 @@ The sections below describe in more detail what happens in the controller Task a
    ``` 
     
    * `name` - The name of Step 1 (`stage_data`).
-   * `base_task_project` and `base_task_name` - The Step 1 base Task to clone (the cloned Task will be executed when the pipeline runs).
+   * `base_task_project` and `base_task_name` - Step 1's base Task to clone (the cloned Task will be executed when the pipeline runs).
 
 1. Add Step 2.    
    
@@ -82,13 +72,12 @@ The sections below describe in more detail what happens in the controller Task a
         post_execute_callback=post_execute_callback_example
    )
    ``` 
-
-                            
-   * `name` - The name of Step 2 (`stage_process`).
-   * `base_task_project` and `base_task_name` - The Step 2 base Task to clone.
-   * `parents` - The start of Step 2 (`stage_process`) depends upon the completion of Step 1 (`stage_data`).
+   In addition to the parameters included in Step 1, input the following: 
+   * `parents` - The names of the steps, which the current step depends upon their completion to begin execution. In this
+     instance, the execution of Step 2 (`stage_process`) depends upon the completion of Step 1 (`stage_data`).
    * `parameter_override` - Pass the URL of the data artifact from Step 1 to Step 2. Override the value of  the parameter 
-     whose key is `dataset_url` (in the parameter group named `General`). Override it with the URL of the artifact named `dataset`. Also override the test size. 
+     whose key is `dataset_url` (in the parameter group named `General`). Override it with the URL of the artifact named 
+     `dataset`. Also override the test size. 
 
     :::important Syntax of the parameter_override Value
     For other examples of ``parameter_override`` syntax, see [PipelineController.add_step](../../references/sdk/automation_controller_pipelinecontroller.md#add_step).
@@ -97,7 +86,8 @@ The sections below describe in more detail what happens in the controller Task a
    * `pre_execute_callback` - The pipeline controller will execute the input callback function before the pipeline step is
    executed. If the callback function returns `False`, the pipeline step will be skipped.  
    *  `post_execute_callback` - The pipeline controller will execute the input callback function after the pipeline step is
-   executed
+   executed.
+      
 1. Add Step 3.
                                       
     ```python
@@ -114,26 +104,27 @@ The sections below describe in more detail what happens in the controller Task a
    * `parameter_override` - Pass the ID of the Step 2 Task to the Step 3 Task. This is the ID of the cloned Task, not the base Task.
     
 1. Run the pipeline.
-      ```python
-      # Starting the pipeline (in the background)
-      pipe.start()
-      ```
+   ```python
+   # Starting the pipeline (in the background)
+   pipe.start()
+   ```
    
 ## Step 1 - Downloading the Data
 
 In the Step 1 Task ([step1_dataset_artifact.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step1_dataset_artifact.py)): 
-1. Clone base Task and enqueue it for execution
+1. Clone base Task and enqueue it for execution using [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely).
    ```python
    task.execute_remotely()
    ```
 
 1. Download data and store it as an artifact named `dataset`. This is the same artifact name used in `parameter_override`
-when the `add_step` method is called in the pipeline controller.
+when the [`add_step`](../../references/sdk/automation_controller_pipelinecontroller.md#add_step) method is called in the pipeline controller.
  
    ```python
    # simulate local dataset, download one, so we have something local
    local_iris_pkl = StorageManager.get_local_copy(
-       remote_url='https://github.com/allegroai/events/raw/master/odsc20-east/generic/iris_dataset.pkl')
+       remote_url='https://github.com/allegroai/events/raw/master/odsc20-east/generic/iris_dataset.pkl'
+   )
     
    # add and upload local file containing our toy dataset
    task.upload_artifact('dataset', artifact_object=local_iris_pkl)
@@ -156,9 +147,10 @@ In the Step 2 Task ([step2_data_processing.py](https://github.com/allegroai/clea
     task.connect(args)
    ```
 
-   The parameter `dataset_url` is the same parameter name used by `parameter_override` when the `add_step` method is called in the pipeline controller.
+   The parameter `dataset_url` is the same parameter name used by `parameter_override` when the [`add_step`](../../references/sdk/automation_controller_pipelinecontroller.md#add_step) 
+   method is called in the pipeline controller.
 
-1. Clone base Task and enqueue it for execution.
+1. Clone base Task and enqueue it for execution using [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely).
    
    ```python
    task.execute_remotely() 
@@ -194,7 +186,7 @@ In the Step 3 Task ([step3_train_model.py](https://github.com/allegroai/clearml/
     
    The parameter `dataset_task_id` is later overridden by the ID of the Step 2 Task (cloned Task, not base Task). 
 
-1. Clone the Step 3 base Task and enqueue it.
+1. Clone the Step 3 base Task and enqueue it using [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely).
    
    ```python
    task.execute_remotely() 
@@ -210,7 +202,7 @@ In the Step 3 Task ([step3_train_model.py](https://github.com/allegroai/clearml/
    y_test = dataset_task.artifacts['y_test'].get()
    ```
     
-1. Train the network and log plots, along with **ClearML** automatic logging.
+1. Train the network and log plots, along with ClearML automatic logging.
 
 ## Running the Pipeline
 
@@ -226,12 +218,13 @@ In the Step 3 Task ([step3_train_model.py](https://github.com/allegroai/clearml/
 
     * Run the script.
      
-            python pipeline_controller.py
+          python pipeline_controller.py
         
-    * Remotely execute the Task - If the Task `pipeline demo` in the project `examples` already exists in **ClearML Server**, clone it and enqueue it to execute.
+    * Remotely execute the Task - If the Task `pipeline demo` in the project `examples` already exists in ClearML Server, clone it and enqueue it to execute.
     
    :::note
-   If you enqueue a Task, a worker must be listening to that queue for the Task to execute.    
+   If you enqueue a Task, make sure an [agent](../../clearml_agent.md) is assigned to the queue, so 
+   it will execute the Task.    
    :::
 
     
diff --git a/docs/guides/pipeline/pipeline_decorator.md b/docs/guides/pipeline/pipeline_decorator.md
new file mode 100644
index 00000000..43f0e895
--- /dev/null
+++ b/docs/guides/pipeline/pipeline_decorator.md
@@ -0,0 +1,101 @@
+---
+title: Pipeline from Decorators
+---
+
+The [pipeline_from_decorator.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_decorator.py) 
+example demonstrates the creation of a pipeline in ClearML using the [`PipelineDecorator`](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)
+class. 
+
+This example creates a pipeline incorporating four tasks, each of which is created from a python function using a custom decorator:
+* `executing_pipeline`- Implements the pipeline controller which defines the pipeline structure and execution logic.
+* `step_one` - Downloads and processes data.
+* `step_two` - Further processes the data from `step_one`.
+* `step_three` - Uses the processed data from `step_two` to train a model.
+
+The pipeline steps, defined in the `step_one`, `step_two`, and `step_three` functions, are each wrapped with the 
+[`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent) 
+decorator, which creates a ClearML pipeline step for each one when the pipeline is executed.
+
+The logic that executes these steps and controls the interaction between them is implemented in the `executing_pipeline` 
+function. This function is wrapped with the [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline) 
+decorator which creates the ClearML pipeline task when it is executed.
+
+The sections below describe in more detail what happens in the pipeline controller and steps.
+
+## Pipeline Controller
+
+In this example, the pipeline controller is implemented by the `executing_pipeline` function.
+
+Using the `@PipelineDecorator.pipeline` decorator creates a ClearML Controller Task from the function when it is executed. 
+For detailed information, see [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline). 
+
+In the example script, the controller defines the interactions between the pipeline steps in the following way:
+1. The controller function passes its argument, `pickle_url`, to the pipeline's first step (`step_one`)
+1. The returned data from the first step, `data_frame`, is passed to `step_two`
+1. The second step's output, `preprocessed_data`, is modified within the pipeline execution logic 
+1. The modified data is passed to the third step, `step_three`.
+
+## Pipeline Steps 
+Using the `@PipelineDecorator.component` decorator will make the function a pipeline component that can be called from the 
+pipeline controller, which implements the pipeline's execution logic. For detailed information, see [`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent). 
+
+When the pipeline controller calls a pipeline step, a corresponding ClearML task will be created. For this reason, each 
+function which makes up a pipeline step needs to be self-contained. Notice that all package imports inside the function 
+will be automatically logged as required packages for the pipeline execution step.
+
+## Pipeline Execution
+
+```python
+PipelineDecorator.set_default_execution_queue('default')
+
+# PipelineDecorator.debug_pipeline()
+
+executing_pipeline(
+    pickle_url='https://github.com/allegroai/events/raw/master/odsc20-east/generic/iris_dataset.pkl',
+)
+```
+
+By default, the pipeline controller and the pipeline steps are launched through ClearML [queues](../../fundamentals/agents_and_queues.md#what-is-a-queue). 
+Use the [`PipelineDecorator.set_default_execution_queue`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
+method to specify the execution queue of all pipeline steps. The` execution_queue` parameter of the `PipelineDecorator.component` 
+decorator overrides the default queue value for the specific step for which it was specified.
+
+:::note Execution Modes
+ClearML provides different pipeline execution modes to accommodate development and production use cases. For additional 
+details, see [Execution Modes](../../fundamentals/pipelines.md#pipeline-controller-execution-options).
+:::
+
+To run the pipeline, call the pipeline controller function.
+
+## WebApp
+
+### Pipeline Controller
+The pipeline controller’s **CONFIGURATION** page contains the pipeline structure and step definitions in its **Configuration Objects** 
+section.
+
+The **Pipeline** configuration object contains the pipeline structure and execution parameters.
+
+![Pipeline configuration](../../img/pipeline_decorator_configurations.png)
+
+An additional configuration object per pipeline step contains the step’s definitions and execution parameters.
+
+The pipeline controller’s **RESULTS > PLOTS** page provides summary details for the pipeline execution.
+
+The **Execution Flow** graphically summarizes the pipeline's execution. Hover over each step to view its details.
+
+![Pipeline execution flow plot](../../img/pipeline_decorator_plot_1.png)
+
+The **Execution Details** table provides the pipeline execution details in table format. 
+
+![Pipeline execution details plot](../../img/pipeline_decorator_plot_2.png)
+
+### Pipeline Steps 
+Each function step’s arguments are stored in their respective task’s **CONFIGURATION > HYPER PARAMETERS > kwargs**. 
+
+![Pipeline step configuration](../../img/pipeline_decorator_step_configuration.png)
+
+
+Values that were listed in the `return_values`parameter of the `PipelineDecorator.component` decorator are stored as 
+artifacts in the relevant step's task. These artifacts can be viewed in the step task’s **ARTIFACTS** tab. 
+
+![Pipeline step artifacts](../../img/pipeline_decorator_step_artifacts.png)
diff --git a/docs/guides/pipeline/pipeline_functions.md b/docs/guides/pipeline/pipeline_functions.md
new file mode 100644
index 00000000..02d7e6c9
--- /dev/null
+++ b/docs/guides/pipeline/pipeline_functions.md
@@ -0,0 +1,137 @@
+---
+title: Pipeline from Functions
+---
+
+The [pipeline_from_functions.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_functions.py)
+example script demonstrates the creation of a pipeline using the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md) 
+class.
+
+This example creates a pipeline incorporating four tasks, each of which is created from a function:
+* `executing_pipeline`- Implements the pipeline controller which defines the pipeline structure and execution logic.
+* `step_one` - Downloads and processes data.
+* `step_two` - Further processes the data from `step_one`.
+* `step_three` - Uses the processed data from `step_two` to train a model.
+
+The step functions will be registered as pipeline steps when they are added to the pipeline controller. The pipeline 
+execution logic is defined in the pipeline controller function. 
+
+When the pipeline steps are executed, corresponding ClearML Tasks are created. For this reason, each function which makes 
+up a pipeline step needs to be self-contained. Notice that all package imports inside the function will be automatically 
+logged as required packages for the pipeline execution step.
+
+
+
+## Pipeline Controller
+
+1. Create the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md) object.
+
+   ```python
+   pipe = PipelineController(
+        name='pipeline demo',
+        project='examples',
+        version='0.0.1',
+        add_pipeline_tags=False,
+   )
+   ```
+    
+1. Set the default execution queue to be used. All the pipeline steps will be enqueued for execution in this queue 
+   (unless overridden by the `execution_queue` parameter of  the `add_function_step` method).
+    
+  ```python
+  pipe.set_default_execution_queue('default')
+  ```
+   
+1. Add a pipeline level parameter that can be referenced from any step in the pipeline (see `step_one` below).
+   ```python
+   pipe.add_parameter(
+        name='url',
+        description='url to pickle file',
+        default='https://github.com/allegroai/events/raw/master/odsc20-east/generic/iris_dataset.pkl'
+   ) 
+   ```
+   
+1. Build the pipeline (see [`PipelineController.add_function_step`](../../references/sdk/automation_controller_pipelinecontroller.md#add_function_step) 
+   for complete reference).
+   
+   The first step in the pipeline uses the `step_one` function and uses as its input the pipeline level argument defined 
+   above. Its return object will be stored as an artifact under the name `data_frame`.
+
+   ```python
+   pipe.add_function_step(
+        name='step_one',
+        function=step_one,
+        function_kwargs=dict(pickle_data_url='${pipeline.url}'),
+        function_return=['data_frame'],
+        cache_executed_step=True,
+   ) 
+   ```
+   
+   The second step in the pipeline uses the `step_two` function and uses as its input the first step’s output.This reference 
+   implicitly defines the pipeline structure, making `step_one` the parent step of `step_two`. 
+   
+   Its return object will be stored as an artifact under the name `processed_data`.
+ 
+   ```python
+   pipe.add_function_step(
+        name='step_two',
+        # parents=['step_one'],  # the pipeline will automatically detect the dependencies based on the kwargs inputs
+        function=step_two,
+        function_kwargs=dict(data_frame='${step_one.data_frame}'),
+        function_return=['processed_data'],
+        cache_executed_step=True,
+   )
+   ```
+   
+   The third step in the pipeline uses the `step_three` function and uses as its input the second step’s output. This 
+   reference implicitly defines the pipeline structure, making `step_two`the parent step of `step_three`.
+   
+   Its return object will be stored as an artifact under the name `model`:
+   
+   ```python
+   pipe.add_function_step(
+        name='step_three',
+        # parents=['step_two'],  # the pipeline will automatically detect the dependencies based on the kwargs inputs
+        function=step_three,
+        function_kwargs=dict(data='${step_two.processed_data}'),
+        function_return=['model'],
+        cache_executed_step=True,
+    )
+   ```
+ 
+1. Run the pipeline. 
+   ```python
+   pipe.start()
+   ```
+   
+   The pipeline will be launched remotely, through the `services` queue, unless otherwise specified.  
+   
+## WebApp
+### Pipeline Controller
+The pipeline controller’s **CONFIGURATION** page contains the pipeline structure and step definitions in its **Configuration Objects** 
+section.
+
+The **Pipeline** configuration object contains the pipeline structure and execution parameters.
+
+![Pipeline configuration](../../img/pipeline_function_config.png)
+
+An additional configuration object per pipeline step contains the step’s definitions and execution parameters.
+
+The pipeline controller’s **RESULTS > PLOTS** page provides summary details for the pipeline execution.
+
+The **Execution Flow** graphically summarizes the pipeline's execution. Hover over each step to view its details.
+
+![Pipeline execution flow plot](../../img/pipeline_decorator_plot_1.png)
+
+The **Execution Details** table provides the pipeline execution details in table format. 
+
+![pipeline execution details plot](../../img/pipeline_function_plot.png)
+
+### Pipeline Steps 
+Each function step’s arguments are stored in their respective task’s **CONFIGURATION > HYPER PARAMETERS > kwargs**. 
+
+![Pipeline step configurations](../../img/pipeline_function_step_configuration.png)
+
+Values that were listed in the `return_values`parameter of the `PipelineDecorator.component` decorator are stored as 
+artifacts in the relevant step's task. These artifacts can be viewed in the step task’s ARTIFACTS tab. 
+
+![Pipeline step artifacts](../../img/pipeline_decorator_step_artifacts.png)
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diff --git a/sidebars.js b/sidebars.js
index 962066cd..b5076543 100644
--- a/sidebars.js
+++ b/sidebars.js
@@ -97,7 +97,7 @@ module.exports = {
             {'IDEs': ['guides/ide/remote_jupyter_tutorial', 'guides/ide/integration_pycharm', 'guides/ide/google_colab']},
             {'Offline Mode':['guides/set_offline']},
             {'Optimization': ['guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt']},
-            {'Pipelines': ['guides/pipeline/pipeline_controller']},
+            {'Pipelines': ['guides/pipeline/pipeline_controller', 'guides/pipeline/pipeline_decorator', 'guides/pipeline/pipeline_functions']},
 
             {'Reporting': ['guides/reporting/explicit_reporting','guides/reporting/3d_plots_reporting', 'guides/reporting/artifacts', 'guides/reporting/using_artifacts', 'guides/reporting/clearml_logging_example', 'guides/reporting/html_reporting',
                 'guides/reporting/hyper_parameters', 'guides/reporting/image_reporting', 'guides/reporting/manual_matplotlib_reporting', 'guides/reporting/media_reporting',