Updated CodeFlare Pipelines description.

Author: chcost

README.md

@@ -119,9 +119,8 @@ Click on the link below to try CodeFlare, on a sandbox environment, without havi
 <img src="./images/pipelines.svg" width="296" height="180">
 </p>
 
-## CodeFlare Pipelines
 
-CodeFlare Pipelines reimagined pipelines to provide a more intuitive API for the data scientist to create AI/ML pipelines, data workflows, pre-processing, post-processing tasks, and many more which can scale from a laptop to a cluster seamlessly.
+**CodeFlare Pipelines** reimagined pipelines to provide a more intuitive API for the data scientist to create AI/ML pipelines, data workflows, pre-processing, post-processing tasks, and many more which can scale from a laptop to a cluster seamlessly.
 
 See the API documentation [here](https://codeflare.readthedocs.io/en/latest/codeflare.pipelines.html), and reference use case documentation in the Examples section.
 
@@ -145,11 +144,11 @@ jupyter-lab codeflare/notebooks/<example_notebook>
 
 The step above should automatically open a browser window and connect to a running Jupyter server.
 
-If you are using any one of the recommended cloud based deployments, examples are found in the `codeflare/notebooks` directory in the container image. The examples can be executed directly from the Jupyter environment. 
+If you are using any one of the recommended cloud based deployments (see below), examples are found in the `codeflare/notebooks` directory in the container image. The examples can be executed directly from the Jupyter environment. 
 
 As a first example of the API usage, see the [sample pipeline](https://github.com/project-codeflare/codeflare/blob/main/notebooks/sample_pipeline.ipynb). 
 
-For an example of how CodeFlare Pipelines can be used to scale out and speed up common machine learning problems, see the [grid search](https://github.com/project-codeflare/codeflare/blob/develop/notebooks/Grid%20Search%20Sample.ipynb) example. It shows how hyperparameter optimization for a reference pipeline can be scaled with both task and data parallelism.
+For an example of how CodeFlare Pipelines can be used to scale out common machine learning problems, see the [grid search](https://github.com/project-codeflare/codeflare/blob/develop/notebooks/Grid%20Search%20Sample.ipynb) example. It shows how hyperparameter optimization for a reference pipeline can be scaled and accelerated with both task and data parallelism.
 
 ## Deploy and integrate anywhere
 

docs/source/pipelines/pipelines_overview.md

@@ -44,8 +44,8 @@ jupyter-lab codeflare/notebooks/<example_notebook>
 
 The step above should automatically open a browser window and connect to a running Jupyter server.
 
-If you are using any one of the recommended cloud based deployments, examples are found in the `codeflare/notebooks` directory in the container image. The examples can be executed directly from the Jupyter environment. 
+If you are using any one of the recommended cloud based deployments (see below), examples are found in the `codeflare/notebooks` directory in the container image. The examples can be executed directly from the Jupyter environment. 
 
 As a first example of the API usage, see the [sample pipeline](https://github.com/project-codeflare/codeflare/blob/main/notebooks/sample_pipeline.ipynb). 
 
-For an example of how CodeFlare Pipelines can be used to scale out and speed up common machine learning problems, see the [grid search](https://github.com/project-codeflare/codeflare/blob/develop/notebooks/Grid%20Search%20Sample.ipynb) example. It shows how hyperparameter optimization for a reference pipeline can be scaled with both task and data parallelism.
+For an example of how CodeFlare Pipelines can be used to scale out common machine learning problems, see the [grid search](https://github.com/project-codeflare/codeflare/blob/develop/notebooks/Grid%20Search%20Sample.ipynb) example. It shows how hyperparameter optimization for a reference pipeline can be scaled and accelerated with both task and data parallelism.