**Hands-on Lab 3: Build a Complete ML Pipeline Using AutoML and Designer**
**🧭 Objective**
In this lab, you will:

Use Azure Machine Learning Designer for a visual ML pipeline

Build and evaluate a model using AutoML

Compare both methods

Deploy the best model


**
🧱 Prerequisites**

An active Azure subscription

Azure Machine Learning Workspace already created

Compute instance and compute cluster set up

A basic dataset (e.g., Titanic, Diabetes, or Bank Marketing)


**
⚙️ PART 1: Create a Dataset in Azure ML**

Go to Azure ML Studio

Navigate to "Data" > + Create

Choose "From local files" or "Web URL" and upload your dataset (e.g., diabetes.csv)

Set type: Tabular

Choose your default datastore

Confirm schema and create the dataset

**🧪 PART 2: AutoML Pipeline**

🧩 Step 1: Launch AutoML
In the Azure ML Studio, go to "Automated ML"

Click + New Automated ML run

Select your uploaded dataset

Select or create a new experiment

Choose a compute cluster

🧪 Step 2: Configure AutoML

Task type: Choose (e.g., Classification if using Titanic)

Target column: e.g., Survived

Accept default preprocessing

Set Exit criteria (e.g., 1 hour or 20 iterations)

▶️ Step 3: Start Run
Click Start

Wait for the experiment to finish (AutoML will try many algorithms and preprocessing pipelines)

**🧠 PART 3: Build the Same Model Using Designer**

📊 Step 1: Open Designer
In Azure ML Studio, go to "Designer"

Click + New pipeline

📦 Step 2: Drag Components
From the left panel, drag the following:

Dataset (your uploaded dataset)

Select Columns in Dataset

Clean Missing Data (optional)

Split Data

Train Model

Score Model

Evaluate Model

Connect components in this order:

mathematica
Copy
Edit
Dataset → Select Columns → Clean Missing → Split
Split → Train Model (also needs Label column)
Train → Score → Evaluate
Choose a built-in algorithm from "Model" tab, e.g., Logistic Regression.

🛠 Step 3: Configure Parameters
In Train Model, choose the label column

In Split Data, use 0.7/0.3 ratio

Select scoring metric: e.g., accuracy

▶️ Step 4: Submit Job
Select compute target

Click Submit

Wait for job to finish

View metrics in Evaluate Model

**
🚀 PART 4: Register and Deploy the Best Model**
From AutoML results or Designer output, click "Register Model"

Go to "Models" tab in left nav

Click your registered model → Deploy

Choose Real-time endpoint

Select compute type (e.g., Azure Kubernetes Service or Managed Online Endpoint)

Add sample input data for testing

📈 PART 5: Compare & Document
Method	Accuracy	Time	Model Name
AutoML	0.89	25m	AutoML_001
Designer	0.85	10m	Logistic_001

📁 Deliverables
✅ A completed visual pipeline in Designer
✅ An AutoML experiment with leaderboard
✅ A registered and deployed model
✅ A short report comparing the two methods

🧼 Cleanup
Delete compute cluster if no longer needed

Delete endpoints to avoid charges