how to create an azure machine learning model

README.md

@@ -15,6 +15,7 @@ Last updated: 2025-04-29
 - Terraform [Demonstration: Deploying Azure Resources for a Data Platform (Microsoft Fabric)](./infrastructure/msFabric/)
 - Terraform [Demonstration: Deploying Azure Resources for an ML Platform](./infrastructure/azMachineLearning/)
 - [Demostration: How to integrate AI in Microsoft Fabric](./msFabric-AI_integration/)
+- [Demostration: Creating a Machine Learning Model](./azML-modelcreation/)
 
 > Azure Machine Learning (PaaS) is a cloud-based platform from Microsoft designed to help `data scientists and machine learning engineers build, train, deploy, and manage machine learning models at scale`. It supports the `entire machine learning lifecycle, from data preparation and experimentation to deployment and monitoring.` It provides powerful tools for `both code-first and low-code users`, including Jupyter notebooks, drag-and-drop interfaces, and automated machine learning (AutoML). `Azure ML integrates seamlessly with other Azure services and supports popular frameworks like TensorFlow, PyTorch, and Scikit-learn.`
 
@@ -228,18 +229,6 @@ Read more about [Endpoints for inference in production](https://learn.microsoft.
 | **Attached Compute** | External compute resources manually connected to Azure ML. | Leverage existing infrastructure. | Using Azure VMs, Databricks, or on-prem compute. | Flexibility, hybrid cloud support, reuse of existing resources. |
 | **Serverless Instances** | Lightweight, on-demand compute (e.g., Azure Container Instances). | Quick testing and low-scale inference. | Temporary model deployment, dev/test environments. | No infrastructure management, fast startup, cost-effective. |
 
-> How to create a Compute Instance: 
-
-1. **Go to Azure Machine Learning Studio**: Navigate to [ml.azure.com](https://ml.azure.com/) and select your workspace.
-2. **Select `Compute` from the left menu**  Choose the **`Compute instances`** tab.
-3. **Click `New`**  
-   - Enter a name for your compute instance.
-   - Choose a virtual machine size (e.g., `Standard_DS3_v2`).
-   - Optionally, enable SSH access or assign a user.
-4. **Click `Create`**: Azure will provision the compute instance, which may take a few minutes.
-
-    https://github.com/user-attachments/assets/bd5f3ce6-7082-4741-8827-8b344cd249a4
-
 </details>
 
 <details>

azML-modelcreation/README.md

@@ -0,0 +1,158 @@
+# Demostration: Creating a Machine Learning Model
+
+Costa Rica
+
+[![GitHub](https://img.shields.io/badge/--181717?logo=github&logoColor=ffffff)](https://github.com/)
+[brown9804](https://github.com/brown9804)
+
+Last updated: 2025-04-29
+
+------------------------------------------
+
+
+<details>
+<summary><b>List of References </b> (Click to expand)</summary>
+
+</details>
+
+<details>
+<summary><b>Table of Content </b> (Click to expand)</summary>
+
+</details>
+
+## Step 1: Set Up Your Azure ML Workspace
+
+> You can use the azure portal approach:
+
+- Go to the [Azure Portal](https://portal.azure.com/)
+- Create a **Machine Learning workspace**:
+  - Resource group
+  - Workspace name
+  - Region
+- Once created, launch **Azure Machine Learning Studio**.
+
+https://github.com/user-attachments/assets/c199156f-96cf-4ed0-a8b5-c88db3e7a552
+
+> Or using terraform configurations for setting up an Azure Machine Learning workspace along with compute clusters and supportive resources to form the core of an ML platform, click here to see [Demonstration: Deploying Azure Resources for an ML Platform](./infrastructure/azMachineLearning/README.md)
+
+## Step 2: Create a Compute Instance
+
+1. **Go to [Azure Machine Learning Studio](https://ml.azure.com/)** and select your workspace.
+2. **Select `Compute` from the left menu**  Choose the **`Compute instances`** tab.
+3. **Click `New`**  
+   - Enter a name for your compute instance.
+   - Choose a virtual machine size (e.g., `Standard_DS3_v2`).
+   - Optionally, enable SSH access or assign a user.
+4. **Click `Create`**: Azure will provision the compute instance, which may take a few minutes.
+
+    https://github.com/user-attachments/assets/bd5f3ce6-7082-4741-8827-8b344cd249a4
+
+## Step 3: Prepare Your Data
+
+- Upload your dataset to **Azure ML datastore** or connect to exrnal sources (e.g., Azure Blob Storage, SQL, etc.).
+- Use **Data > Datasets** to register and version your dataset.
+
+> For example: Upload the CSV to Azure ML
+
+1. Under to **Data > + Create > From local files**.
+2. Choose:
+   - **Name**: `employee_data`
+   - **Type**: Tabular
+   - **Browse** and upload the [sample_data.csv](./azML-modelcreation/data/sample_data.csv) file.
+3. Click **Next**, then **Review + Create**.
+
+> Register the Dataset: 
+
+1. After upload, Azure will preview the data.
+2. Confirm the schema (column names and types).
+3. Click **Create** to register the dataset.
+
+https://github.com/user-attachments/assets/f8cbd32c-94fc-43d3-a7a8-00f63cdc543d
+
+
+### **4. Create a New Notebook or Script**
+- Use the compute instance to open a **Jupyter notebook** or create a Python script.
+- Import necessary libraries:
+  ```python
+  import pandas as pd
+  from sklearn.model_selection import train_test_split
+  from sklearn.ensemble import RandomForestClassifier
+  from sklearn.metrics import accuracy_score
+  ```
+
+---
+
+### **5. Load and Explore the Data**
+- Load the dataset and perform basic EDA (exploratory data analysis):
+  ```python
+  data = pd.read_csv('your_dataset.csv')
+  print(data.head())
+  ```
+
+---
+
+### **6. Train Your Model**
+- Split the data and train a model:
+  ```python
+  X = data.drop('target', axis=1)
+  y = data['target']
+  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+  model = RandomForestClassifier()
+  model.fit(X_train, y_train)
+  ```
+
+---
+
+### **7. Evaluate the Model**
+- Check performance:
+  ```python
+  predictions = model.predict(X_test)
+  print("Accuracy:", accuracy_score(y_test, predictions))
+  ```
+
+---
+
+### **8. Register the Model**
+- Save and register the model in Azure ML:
+  ```python
+  import joblib
+  joblib.dump(model, 'model.pkl')
+
+  from azureml.core import Workspace, Model
+  ws = Workspace.from_config()
+  Model.register(workspace=ws, model_path="model.pkl", model_name="my_model")
+  ```
+
+---
+
+### **9. Deploy the Model**
+- Create an **inference configuration** and deploy to a web service:
+  ```python
+  from azureml.core.environment import Environment
+  from azureml.core.model import InferenceConfig
+  from azureml.core.webservice import AciWebservice
+
+  env = Environment.from_conda_specification(name="myenv", file_path="env.yml")
+  inference_config = InferenceConfig(entry_script="score.py", environment=env)
+
+  deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)
+  service = Model.deploy(workspace=ws,
+                         name="my-service",
+                         models=[model],
+                         inference_config=inference_config,
+                         deployment_config=deployment_config)
+  service.wait_for_deployment(show_output=True)
+  ```
+
+---
+
+### **10. Test the Endpoint**
+- Once deployed, you can send HTTP requests to the endpoint to get predictions.
+
+
+
+<div align="center">
+  <h3 style="color: #4CAF50;">Total Visitors</h3>
+  <img src="https://profile-counter.glitch.me/brown9804/count.svg" alt="Visitor Count" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>
+</div>

azML-modelcreation/data/README.md

@@ -0,0 +1,25 @@
+# Sample Data 
+
+Costa Rica
+
+[![GitHub](https://img.shields.io/badge/--181717?logo=github&logoColor=ffffff)](https://github.com/)
+[brown9804](https://github.com/brown9804)
+
+Last updated: 2025-04-29
+
+------------------------------------------
+
+> This dataset is a synthetic sample of employee records created for demonstration and training purposes in a machine learning workflow. This sample data is designed to simulate a
+> realistic HR dataset and will be used to build and evaluate a machine learning model for tasks such as salary prediction, employee segmentation, or attrition analysis.
+> It contains > 50,000 rows with the following attributes:
+
+- **Name**: A randomly selected first name.
+- **Age**: An integer representing the employee's age (22–65).
+- **Department**: The department where the employee works (e.g., HR, Engineering, Sales).
+- **Salary**: The employee's annual salary in USD, ranging from \$40,000 to \$120,000.
+
+
+<div align="center">
+  <h3 style="color: #4CAF50;">Total Visitors</h3>
+  <img src="https://profile-counter.glitch.me/brown9804/count.svg" alt="Visitor Count" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>
+</div>