diff --git a/docs/get-started/create_pipeline.md b/docs/get-started/create_pipeline.md
index 04ac62a2..8e40971e 100644
--- a/docs/get-started/create_pipeline.md
+++ b/docs/get-started/create_pipeline.md
@@ -3,23 +3,23 @@
:fontawesome-brands-youtube:{ .youtube }
Check this quickstart video on how to create your first Pipeline.
-The best way to get started with Pipelines is to use the interactive Pipeline editor available in the Labs with Jupyter Lab set as IDE.
+The best way to get started with Pipelines is to use the interactive Pipeline editor available in the Labs with Jupyter Lab set as IDE.
If you don't have a **Lab** yet, or you don't know how to create one, check our quickstart guide on how to create your first lab.
Open an already existing lab.
A Pipeline comprises one or more nodes that are connected (or not!) with each other to define execution dependencies. Each pipeline node
is and should be implemented as a component that is expected to manage a single task, such as read the data, profiling the data, training a model,
-or even publishing a model to production environments.
+or even publishing a model to production environments.
-In this tutorial we will build a simple and generic pipeline that use a **Dataset** from Fabric's **Data Catalog** and profile to check it's quality.
+In this tutorial we will build a simple and generic pipeline that use a **Dataset** from Fabric's **Data Catalog** and profile to check it's quality.
We have the notebooks template already available. For that you need to access the *"Academy"* folder as per the image below.
-Make sure to copy all the files in the folder "3 - Pipelines/quickstart" to the root folder of your lab, as per the image below.
+Make sure to copy all the files in the folder "3 - Pipelines/quickstart" to the root folder of your lab, as per the image below.
@@ -38,18 +38,18 @@ The following screen will be shown. Click in copy.
-Now that we have copied the code, let's get back to our **"1. Read data.ipynb"** notebook, and replace the first code cell by with the new code. This will allow us to use a
-dataset from the Data Catalog in our pipeline.
+Now that we have copied the code, let's get back to our **"1. Read data.ipynb"** notebook, and replace the first code cell by with the new code. This will allow us to use a
+dataset from the Data Catalog in our pipeline.
-With our notebooks ready, we can now configure our **Pipeline**.
+With our notebooks ready, we can now configure our **Pipeline**.
For this quickstart we will be leveraging an already existing pipeline - double-click the file *my_first_pipeline.pipeline*. You should see a pipeline
as depicted in the images below.
-To create a new Pipeline, you can open the lab launcher tab and select **"Pipeline Editor"**.
+To create a new Pipeline, you can open the lab launcher tab and select **"Pipeline Editor"**.
@@ -57,15 +57,15 @@ To create a new Pipeline, you can open the lab launcher tab and select **"Pipeli
Before running the pipeline, we need to check each component/step properties and configurations. Right-click each one of the steps, select *"Open Properties"*, and a
-menu will be depicted in your right side. Make sure that you have *"YData - CPU"* selected as the **Runtime Image** as show below.
+menu will be depicted in your right side. Make sure that you have *"YData - CPU"* selected as the **Runtime Image** as show below.
-We are now ready to create and run our first pipeline. In the top left corner of the pipeline editor, the run button
-will be available for you to click.
+We are now ready to create and run our first pipeline. In the top left corner of the pipeline editor, the run button
+will be available for you to click.
@@ -77,7 +77,7 @@ Accept the default values shown in the run dialog and start the run
-If the following message is shown, it means that you have create a run of your first pipeline.
+If the following message is shown, it means that you have create a run of your first pipeline.
@@ -95,23 +95,19 @@ Your most recent pipeline will be listed, as shown in below image.
-To check the run of your pipeline, jump into the **"Run"** tab. You will be able to see your first pipeline running!
+To check the run of your pipeline, jump into the **"Run"** tab. You will be able to see your first pipeline running!
By clicking on top of the record you will be able to see the progress of the run step-by-step, and visualize the outputs of each and every
-step by clicking on each step and selecting the **Visualizations** tab.
+step by clicking on each step and selecting the **Visualizations** tab.
**Congrats!** 🚀 You have now successfully created your first **Pipeline** a code environment, so you can benefit from Fabric's
-orchestration engine to crate scalable, versionable and comparable data workflows.
+orchestration engine to crate scalable, versionable and comparable data workflows.
Get ready for your journey of improved quality data for AI.
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