Update Modin* name (#2295)

* Update README.md

Update Modin* name by Stefana Raileanu

* Update README.md

Update Modin* name by Stefana Raileanu

* Update sample.json

Update Modin* name by Stefana Raileanu

* Update README.md

Update Modin* name by Stefana Raileanu

* Update sample.json

Author: raistefintel

AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/README.md

@@ -1,11 +1,11 @@
-# Modin Get Started Sample
+# Modin* Get Started Sample
 
-The `Modin Getting Started` sample demonstrates how to use distributed Pandas using the Modin package. 
+The `Modin* Getting Started` sample demonstrates how to use distributed Pandas using the Modin package. 
 
 | Area                  | Description
 | :---                  | :---
 | Category              | Getting Started
-| What you will learn   | Basic Modin programming model for Intel processors
+| What you will learn   | Basic Modin* programming model for Intel processors
 | Time to complete      | 5 to 8 minutes
 
 ## Purpose
@@ -52,7 +52,7 @@ This get started sample code is implemented for CPU using the Python language. T
    conda install ipykernel
    python -m ipykernel install --user --name usr_modin
    ```
-## Run the `Modin Get Started` Sample
+## Run the `Modin* Get Started` Sample
 
 You can run the Jupyter notebook with the sample code on your local server or download the sample code from the notebook as a Python file and run it locally. 
 

AI-and-Analytics/Getting-Started-Samples/Modin_GettingStarted/sample.json

@@ -1,8 +1,8 @@
 {
   "guid": "AE280EFE-9EB1-406D-B32D-5991F707E195",
-  "name": "Intel® Distribution of Modin* Getting Started",
+  "name": "Modin* Getting Started",
   "categories": ["Toolkit/oneAPI AI And Analytics/Getting Started"],
-  "description": "This sample illustrates how to use Modin accelerated Pandas functions and notes the performance gain when compared to standard Pandas functions",
+  "description": "This sample illustrates how to use Modin* accelerated Pandas functions and notes the performance gain when compared to standard Pandas functions",
   "builder": ["cli"],
   "languages": [{"python":{}}],
   "os":["linux"],
@@ -19,7 +19,7 @@
 			"conda activate intel-aikit-modin",
 			"pip install -r requirements.txt # Installing notebook's dependencies",
 			"pip install runipy # Installing 'runipy' for extended abilities to execute the notebook",
-			"runipy Modin_GettingStarted.ipynb # Test 'Modin is faster than pandas' case",
+			"runipy Modin_GettingStarted.ipynb # Test 'Modin* is faster than pandas' case",
 			"MODIN_CPUS=1 runipy Modin_GettingStarted.ipynb # Test 'Modin is slower than pandas' case"
   		]
   	}

AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/README.md

@@ -1,11 +1,11 @@
-# Modin Vs. Pandas Performance Sample
+# Modin* Vs. Pandas Performance Sample
 
-The `Modin Vs. Pandas Performance` code illustrates how to use Modin* to replace the Pandas API. The sample compares the performance of Modin and the performance of Pandas for specific dataframe operations.
+The `Modin* Vs. Pandas Performance` code illustrates how to use Modin* to replace the Pandas API. The sample compares the performance of Modin* and the performance of Pandas for specific dataframe operations.
 
 | Area                       | Description
 |:---                        |:---
 | Category                   | Concepts and Functionality
-| What you will learn        | How to accelerate the Pandas API using Modin.
+| What you will learn        | How to accelerate the Pandas API using Modin*.
 | Time to complete           | Less than 10 minutes
 
 ## Purpose
@@ -19,17 +19,17 @@ You can run the sample locally or in Google Colaboratory (Colab).
 |:---                       |:---
 | OS                        | Ubuntu* 20.04 (or newer)
 | Hardware                  | Intel® Core™ Gen10 Processor <br> Intel® Xeon® Scalable Performance processors
-| Software                  | Intel® Distribution of Modin*
+| Software                  | Modin*
 
 ## Key Implementation Details
 
-This code sample is implemented for CPU using Python programming language. The sample requires NumPy, Pandas, Modin libraries, and the time module in Python.
+This code sample is implemented for CPU using Python programming language. The sample requires NumPy, Pandas, Modin* libraries, and the time module in Python.
 
 ## Environment Setup
 
 If you want to run the sample on a local system using a command-line interface (CLI), you must install the Modin in a new Conda* environment first.
 
-### Install Modin
+### Install Modin*
 
 1. Create a Conda environment.
    ```
@@ -65,7 +65,7 @@ If you want to run the sample on a local system using a command-line interface (
    ipython Modin_Vs_Pandas.ipynb
    ```
 
-## Run the `Modin Vs Pandas Performance` Sample in Google Colaboratory
+## Run the `Modin* Vs Pandas Performance` Sample in Google Colaboratory
 
 1. Change to the directory containing the `Modin_Vs_Pandas.ipynb` notebook file on your local system.
 

AI-and-Analytics/Getting-Started-Samples/Modin_Vs_Pandas/sample.json

@@ -1,8 +1,8 @@
 {
 	"guid": "FE479C5C-C7A0-4612-B8D0-F83D07155411",
-	"name": "Intel® Modin Vs. Pandas Performance",
+	"name": "Modin* Vs. Pandas Performance",
 	"categories": ["Toolkit/oneAPI AI And Analytics/Getting Started"],
-	"description": "This sample code illustrates how Intel® Modin accelerates the performance of Pandas for computational operations on a dataframe.",
+	"description": "This sample code illustrates how Modin* accelerates the performance of Pandas for computational operations on a dataframe.",
 	"builder": ["cli"],
 	"languages": [{
 		"python": {}

AI-and-Analytics/Getting-Started-Samples/README.md

@@ -18,8 +18,8 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 |--------------------------| --------- | ------------------------------------------------ | -
 |Inference Optimization| Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Sample-for-PyTorch](INC-Quantization-Sample-for-PyTorch)                     | Performs INT8 quantization on a Hugging Face BERT model.
 |Inference Optimization| Intel® Neural Compressor (INC) | [Intel® Neural Compressor (INC) Sample-for-Tensorflow](INC-Sample-for-Tensorflow)                     | Quantizes a FP32 model into INT8 by Intel® Neural Compressor (INC) and compares the performance between FP32 and INT8.
-|Data Analytics <br/> Classical Machine Learning  | Modin | [Modin_GettingStarted](Modin_GettingStarted)                     | Run Modin-accelerated Pandas functions and note the performance gain.
-|Data Analytics <br/> Classical Machine Learning | Modin |[Modin_Vs_Pandas](Modin_Vs_Pandas)| Compares the performance of Intel® Distribution of Modin* and the performance of Pandas.
+|Data Analytics <br/> Classical Machine Learning  | Modin* | [Modin_GettingStarted](Modin_GettingStarted)                     | Run Modin*-accelerated Pandas functions and note the performance gain.
+|Data Analytics <br/> Classical Machine Learning | Modin* |[Modin_Vs_Pandas](Modin_Vs_Pandas)| Compares the performance of Intel® Distribution of Modin* and the performance of Pandas.
 |Classical Machine Learning| Intel® Optimization for XGBoost* | [IntelPython_XGBoost_GettingStarted](IntelPython_XGBoost_GettingStarted)                     | Set up and trains an XGBoost* model on datasets for prediction.
 |Classical Machine Learning| daal4py | [IntelPython_daal4py_GettingStarted](IntelPython_daal4py_GettingStarted)                     | Batch linear regression using the Python API package daal4py from oneAPI Data Analytics Library (oneDAL).
 |Deep Learning <br/> Inference Optimization| Intel® Optimization for TensorFlow* | [IntelTensorFlow_GettingStarted](IntelTensorFlow_GettingStarted)               | A simple training example for TensorFlow.