Adding changes from Cameron's test run

072-DataScienceInFabric/Coach/README.md

@@ -45,7 +45,7 @@ This hack requires students to have access to Azure and Fabric. These requiremen
 Each student will need access to Microsoft Fabric and be licensed to create PowerBI reports for this hack. The following are the options to complete these licensing requirements:
 
 1. **Recommended if available**: Individual [Fabric free trials](https://learn.microsoft.com/en-us/fabric/get-started/fabric-trial#start-the-fabric-capacity-trial). This will grant users access to creating the required Fabric items as well as the PowerBI report. **If previously used, the Fabric free trial may be unavailable**
-2. Fabric Capacity and PowerBI Pro/Premium per user license. Each user would need their own PowerBI license but capacities could be shared and scaled up according to the needs. **Alternatively, users can activate a [PowerBI Free Trial](https://learn.microsoft.com/en-us/power-bi/fundamentals/service-self-service-signup-for-power-bi) if available.** The PowerBI trial could be available even if the Fabric one is not.
+2. Fabric Capacity and PowerBI Pro/Premium per user license. Each user would need their own PowerBI license but capacities could be shared and scaled up according to the needs. If running the hack on an individual basis, an F4 capacity would be adequate, and an F8 capacity would have generous compute power margin.  **Alternatively, users can activate a [PowerBI Free Trial](https://learn.microsoft.com/en-us/power-bi/fundamentals/service-self-service-signup-for-power-bi) if available.** The PowerBI trial could be available even if the Fabric one is not.
 
 
 ### Azure licensing requirements

072-DataScienceInFabric/Coach/Solution-00.md

@@ -40,6 +40,7 @@ The [coach solution notebooks](./Solutions/) are the completed versions of the s
 To begin setting up your Azure subscription for this hack, you will run a bash script that will deploy and configure a list of resources. You can find this script as the `HackSetup.sh` file in the resources folder. 
  - Download the setup file to your computer
  - Go to the Azure portal and click on the cloud shell button on the top navigation bar, to the right of the Copilot button.
+  - **NOTE**: This script has been designed for the Azure CLI. It might fail to deploy if you attempt to run it from a local terminal.
  - Once the cloud shell connects, make sure you are using a Bash shell. If you are not, click on the button on the top-right corner of the cloud shell to switch to bash.
  - Click on the Manage Files button on the shell's navigation bar and select upload. Select the setup file from your computer.
  - Run the `sh HackSetup.sh` command in your cloud shell.

072-DataScienceInFabric/Coach/Solution-06.md

@@ -20,7 +20,7 @@
 - Fabric will combine the ML Model along with a Python Object Serialization file (a/k/a pickle file) having a `.pkl` extension, and YAML files (.yml) that describe to other platforms the structure and interface provided by the model. All these files are packaged into a .zip file, which will be placed in your Downloads folder.
 - If you open the .zip file, you can review the model and metadata files before deploying them to other systems. Make sure you extract the folder before continuing, as you will not be able to upload a .zip file.
   ![picture alt](../Images/image-11.png)
-
+**NOTE**: due to some compatibility issues with MLFlow and Azure Machine Learning, you will need to modify some files locally before uploading your model to Azure Machine Learning. Once you have downloaded your model from Fabric, unzip the folder and open both the `conda.yaml` and `requirements.txt` files. Change the MLFlow version on both files to `2.7.0`. Save and close both files.
 - After downloading the model, we have to deploy an Azure ML real-time inference endpoint to publish the model to the Internet via a RESTful web service.
 - Navigate to your Azure Portal. Open the Azure Machine Learning workspace you previously deployed with the script in Challenge 0.
 - Using the left menu in your workspace, navigate to **Assets**>**Models**.

072-DataScienceInFabric/README.md

@@ -34,7 +34,7 @@ In this hack you will be learning how to best leverage Fabric for Data Science.
 	 - Deploy the model you trained to AzureML and generate predictions via the API
 ## Prerequisites
 
-- Microsoft Fabric capacity/trial capacity
+- Microsoft Fabric capacity/trial capacity. If running the hack on an individual basis, an F4 capacity would be adequate, and an F8 capacity would have generous compute power margin.
 - PowerBI Pro or Premium per user subscription/trial (unless using Fabric trial capacity)
 - Access to an Azure subscription to:
   -  Deploy a storage account to store the dataset (alternatively you can upload the dataset directly to Fabric)

072-DataScienceInFabric/Student/Challenge-02.md

@@ -8,7 +8,7 @@ Welcome to Challenge 2! In this exercise, you will learn how to use Data Wrangle
 
 ## Description
 
-The main tasks in this challenge consist in different pre-processing steps that are important for developing robust, efficient and reliable machine learning models, like for example:
+The main tasks in this challenge consist in different pre-processing steps that are important for developing robust, efficient and reliable machine learning models. You must ensure that you accomplish the following tasks during this challenge:
 1. Removing unnecessary columns from a dataset. It is the best practice that enhances model performance, improves interpretability & reduces complexity.
 2. Dropping rows with missing values ensure compatibility with a wide range of algorithms without needing additional imputation strategies.
 3. Handling duplicate rows is an essential step in data preparation because it ensures data quality.

072-DataScienceInFabric/Student/Challenge-06.md

@@ -15,6 +15,8 @@ By the end of this challenge, you should be able to understand and know how to u
 - MLFlow exported models, what the different files represent and how you can import them to a new location.
 - AzureML real-time endpoints, how to create one with a custom model and how to use it via an API call.
 
+**NOTE**: due to some compatibility issues with MLFlow and Azure Machine Learning, you will need to modify some files locally before uploading your model to Azure Machine Learning. Once you have downloaded your model from Fabric, unzip the folder and open both the `conda.yaml` and `requirements.txt` files. Change the MLFlow version on both files to `2.7.0`. Save and close both files.
+
 ## Success Criteria
 
 To complete this challenge, please verify that :
@@ -27,7 +29,7 @@ To complete this challenge, please verify that :
   - [Difference between Data science in Fabric and Azure Machine Learning](https://www.linkedin.com/pulse/comparing-microsoft-fabric-azure-machine-learning-which-kim-berg)
   - [How to deploy MLFlow model to Azure Machine learning](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2&tabs=studio)
 
-## Learning Resources
+## Tips
 
 - Fabric stores everything you create as items in the workspace.
 - Make sure you unzip the folder you will download from Fabric