From 00afb5f8ab922a954b55080be4273c7b82385d99 Mon Sep 17 00:00:00 2001
From: Timna Brown <24630902+brown9804@users.noreply.github.com>
Date: Tue, 29 Apr 2025 08:43:19 -0600
Subject: [PATCH 1/7] how to create an azure machine learning model
---
azML-modelcreation/README.md | 137 +++++++++++++++++++++++++++++++++++
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+# Demostration: Creating a Machine Learning Model
+
+Costa Rica
+
+[](https://github.com/)
+[brown9804](https://github.com/brown9804)
+
+Last updated: 2025-04-29
+
+------------------------------------------
+
+
+List of References (Click to expand)
+
+
+
+
+Table of Content (Click to expand)
+
+
+
+> Azure ML is a cloud-based platform that provides tools for building, training, and deploying ML models at scale.
+
+## 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)
+
+### **2. Create a Compute Instance**
+- In Azure ML Studio, go to **Compute > Compute Instances**.
+- Create a new instance (choose CPU or GPU depending on your needs).
+- This will be your development environment (like a cloud-based Jupyter notebook).
+
+---
+
+### **3. Prepare Your Data**
+- Upload your dataset to **Azure ML datastore** or connect to external sources (e.g., Azure Blob Storage, SQL, etc.).
+- Use **Data > Datasets** to register and version your dataset.
+
+---
+
+### **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.
+
+
+
+
+
Total Visitors
+
+
From 3cc99e218b3dbd8ddf947fec062d577faa7797b2 Mon Sep 17 00:00:00 2001
From: Timna Brown <24630902+brown9804@users.noreply.github.com>
Date: Tue, 29 Apr 2025 08:53:55 -0600
Subject: [PATCH 4/7] working on step 3 - prepare data
---
azML-modelcreation/README.md | 39 +++++++++++++++++++++++++++---------
1 file changed, 30 insertions(+), 9 deletions(-)
diff --git a/azML-modelcreation/README.md b/azML-modelcreation/README.md
index d93a0e8..423f613 100644
--- a/azML-modelcreation/README.md
+++ b/azML-modelcreation/README.md
@@ -9,6 +9,7 @@ Last updated: 2025-04-29
------------------------------------------
+
List of References (Click to expand)
@@ -19,8 +20,6 @@ Last updated: 2025-04-29
-> Azure ML is a cloud-based platform that provides tools for building, training, and deploying ML models at scale.
-
## Step 1: Set Up Your Azure ML Workspace
> You can use the azure portal approach:
@@ -36,17 +35,39 @@ 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)
-### **2. Create a Compute Instance**
-- In Azure ML Studio, go to **Compute > Compute Instances**.
-- Create a new instance (choose CPU or GPU depending on your needs).
-- This will be your development environment (like a cloud-based Jupyter notebook).
+## 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
-### **3. Prepare Your Data**
-- Upload your dataset to **Azure ML datastore** or connect to external sources (e.g., Azure Blob Storage, SQL, etc.).
+- 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` 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.
+
+
---
### **4. Create a New Notebook or Script**
From d1db7b58310456c5f652fac4c2322900b5800b59 Mon Sep 17 00:00:00 2001
From: Timna Brown <24630902+brown9804@users.noreply.github.com>
Date: Tue, 29 Apr 2025 09:01:28 -0600
Subject: [PATCH 5/7] sample data
---
azML-modelcreation/data/README.md | 25 +++++++++++++++++++++++++
1 file changed, 25 insertions(+)
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diff --git a/azML-modelcreation/data/README.md b/azML-modelcreation/data/README.md
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+++ b/azML-modelcreation/data/README.md
@@ -0,0 +1,25 @@
+# Sample Data
+
+Costa Rica
+
+[](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.
+
+
+
+
Total Visitors
+
+