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‎README.md‎

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- Terraform [Demonstration: Deploying Azure Resources for a Data Platform (Microsoft Fabric)](./infrastructure/msFabric/)
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- Terraform [Demonstration: Deploying Azure Resources for an ML Platform](./infrastructure/azMachineLearning/)
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- [Demostration: How to integrate AI in Microsoft Fabric](./msFabric-AI_integration/)
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- [Demostration: Creating a Machine Learning Model](./azML-modelcreation/)
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> 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.`
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| **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. |
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| **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. |
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> How to create a Compute Instance:
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1. **Go to Azure Machine Learning Studio**: Navigate to [ml.azure.com](https://ml.azure.com/) and select your workspace.
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2. **Select `Compute` from the left menu** Choose the **`Compute instances`** tab.
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3. **Click `New`**
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- Enter a name for your compute instance.
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- Choose a virtual machine size (e.g., `Standard_DS3_v2`).
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- Optionally, enable SSH access or assign a user.
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4. **Click `Create`**: Azure will provision the compute instance, which may take a few minutes.
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</details>
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<details>

‎azML-modelcreation/README.md‎

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

‎azML-modelcreation/data/README.md‎

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# Sample Data
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Costa Rica
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[![GitHub](https://img.shields.io/badge/--181717?logo=github&logoColor=ffffff)](https://github.com/)
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[brown9804](https://github.com/brown9804)
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Last updated: 2025-04-29
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------------------------------------------
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> 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
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> 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.
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> It contains > 50,000 rows with the following attributes:
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- **Name**: A randomly selected first name.
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- **Age**: An integer representing the employee's age (22–65).
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- **Department**: The department where the employee works (e.g., HR, Engineering, Sales).
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- **Salary**: The employee's annual salary in USD, ranging from \$40,000 to \$120,000.
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<div align="center">
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<h3 style="color: #4CAF50;">Total Visitors</h3>
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<img src="https://profile-counter.glitch.me/brown9804/count.svg" alt="Visitor Count" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>
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</div>

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