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Author: brown9804

azML-modelcreation/README.md

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+# Demostration: Creating a Machine Learning Model
+
+Costa Rica
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+[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>
+
+> 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.
+
+
+
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