added a simple workflow

.github/workflows/mlops-workflow.yml

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+name: ML Ops Workflow
+
+on: [push]
+
+env:
+    workspace-name: aml-ws-mlops
+    resource-group: rg-mlops
+
+jobs:
+  build-and-deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.8'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r ./ml/requirements.txt
+
+    - name: Configure Azure credentials
+      uses: azure/login@v1
+      with:
+        client-id: ${{ secrets.AZURE_CLIENT_ID }}
+        tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+        subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+
+    - name: Set up Azure Machine Learning
+      run: |
+        az extension add -n azure-cli-ml
+        az ml workspace configure -w your-workspace-name -g your-resource-group --subscription your-subscription-id
+
+    - name: Upload dataset
+      run: |
+        az ml data create -f data.yaml
+
+    - name: Submit training run
+      run: |
+        az ml job create --file train.yaml
+
+    # - name: Register the model
+    #   run: |
+    #     az ml job create --file register_model.yaml
+
+    # - name: Deploy the model
+    #   run: |
+    #     az ml job create --file deploy_model.yaml
+
+    # - name: Test the deployment
+    #   run: |
+    #     python test_deployment.py

ml/data.yaml

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+$schema: http://azuremlsdk2.blob.core.windows.net/latest/data.schema.json
+name: attrition_data
+version: 1
+type: uri_file
+path: ./employee_attrition.csv

ml/deploy_model.py

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+from azureml.core import Workspace, Model, Environment
+from azureml.core.webservice import AciWebservice, Webservice
+from azureml.core.model import InferenceConfig
+
+# Initialize workspace
+ws = Workspace.from_config()
+
+# Get the registered model
+model = Model(ws, "employee_attrition_model")
+
+# Create environment
+env = Environment.from_conda_specification(
+    name="project_environment", file_path="environment.yml"
+)
+
+# Create inference configuration
+inference_config = InferenceConfig(entry_script="score.py", environment=env)
+
+# Define the deployment configuration
+aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)
+
+# Deploy the model
+service = Model.deploy(
+    workspace=ws,
+    name="attrition-service",
+    models=[model],
+    inference_config=inference_config,
+    deployment_config=aci_config,
+)
+service.wait_for_deployment(show_output=True)

ml/deploy_model.yaml

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+$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
+command: >
+  python deploy_model.py
+environment: azureml:project_environment:1
+inputs:
+  attrition_data:
+    data: azureml:attrition_data:1
+compute: azureml:cpu-cluster
+experiment_name: deploy_model_experiment
+output:
+  name: outputs
+  type: uri_folder
+  mode: Upload

ml/employee_attrition.csv

@@ -0,0 +1,6 @@
+EmployeeNumber,Age,Department,DistanceFromHome,Education,Gender,JobRole,MaritalStatus,MonthlyIncome,NumCompaniesWorked,PercentSalaryHike,TotalWorkingYears,TrainingTimesLastYear,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager,Attrition
+1,41,Sales,1,2,Male,Sales Executive,Single,5993,8,11,8,0,6,4,0,5,Yes
+2,49,Research & Development,8,1,Female,Research Scientist,Married,5130,1,23,10,3,10,7,1,7,No
+3,37,Research & Development,2,2,Male,Laboratory Technician,Single,2090,6,15,7,3,0,0,0,8,Yes
+4,33,Research & Development,3,4,Female,Research Scientist,Married,2909,1,11,8,3,8,7,3,0,8,No
+5,27,Research & Development,2,1,Male,Laboratory Technician,Married,3468,9,12,6,3,2,2,2,2,Yes

ml/environment.yml

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+name: project_environment
+dependencies:
+  - python=3.8
+  - scikit-learn
+  - pandas
+  - numpy
+  - pip
+  - pip:
+    - azureml-sdk
+    - joblib

ml/register_model.py

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+import joblib
+from azureml.core import Run, Model
+
+run = Run.get_context()
+
+# Register the model
+model = Model.register(
+    workspace=run.experiment.workspace,
+    model_path="outputs/model.joblib",
+    model_name="employee_attrition_model",
+)

ml/register_model.yaml

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+$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
+command: >
+  python register_model.py
+environment: azureml:project_environment:1
+inputs:
+  attrition_data:
+    data: azureml:attrition_data:1
+compute: azureml:cpu-cluster
+experiment_name: register_model_experiment
+output:
+  name: outputs
+  type: uri_folder
+  mode: Upload

ml/score.py

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+import json
+import joblib
+import numpy as np
+from azureml.core.model import Model
+
+
+def init():
+    global model
+    model_path = Model.get_model_path("employee_attrition_model")
+    model = joblib.load(model_path)
+
+
+def run(data):
+    try:
+        data = np.array(json.loads(data)["data"])
+        result = model.predict(data)
+        return json.dumps({"prediction": result.tolist()})
+    except Exception as e:
+        error = str(e)
+        return json.dumps({"error": error})

ml/test_deployment.py

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+import requests
+import json
+
+# Replace with your service URI
+scoring_uri = "YOUR_SERVICE_URI"
+
+# Example data for prediction
+data = {"data": [[41, 1, 2, 1, "Male", "Sales Executive", "Single", 5993, 8, 11, 8, 0, 6, 4, 0, 5]]}
+input_data = json.dumps(data)
+
+# Set the content type
+headers = {'Content-Type': 'application/json'}
+
+# Make the request
+response = requests.post(scoring_uri, data=input_data, headers=headers)
+print(response.json())

ml/train.py

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+import os
+import pandas as pd
+from azureml.core import Run, Dataset
+from azureml.core.model import Model
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import joblib
+
+# Get the current run context
+run = Run.get_context()
+
+# Load the dataset
+dataset = run.input_datasets["attrition_data"].to_pandas_dataframe()
+
+# Data preprocessing
+X = dataset.drop(columns=["Attrition"])
+y = dataset["Attrition"].apply(lambda x: 1 if x == "Yes" else 0)
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# Train model
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Evaluate model
+y_pred = model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+run.log("accuracy", accuracy)
+
+# Save the model
+os.makedirs("outputs", exist_ok=True)
+joblib.dump(model, "outputs/model.joblib")
+
+# Register the model
+model = Model.register(
+    workspace=run.experiment.workspace,
+    model_path="outputs/model.joblib",
+    model_name="employee_attrition_model",
+)
+
+run.complete()

ml/train.yaml

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+$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
+command: >
+  python train.py
+environment: azureml:project_environment:1
+inputs:
+  attrition_data:
+    data: azureml:attrition_data:1
+compute: azureml:cpu-cluster
+experiment_name: employee_attrition_experiment
+output:
+  name: outputs
+  type: uri_folder
+  mode: Upload