Case study and tutorial for Amazon SageMaker Studio (Unified Experience)

Case study and tutorial for Amazon SageMaker Studio (Unified Experience), designed to help enterprise teams, data scientists, and ML engineers understand its capabilities, features, and implementation through a real-world example.

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🧠 Case Study: Predicting Loan Defaults Using Amazon SageMaker Studio (Unified Experience)

🏢 Client Profile

Company: Condifential 
Industry: Financial Services
Objective: To build an end-to-end machine learning pipeline to predict loan default risks using Amazon SageMaker Studio Unified Experience.

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🎯 Business Challenge

The company needed:

• A collaborative, scalable, and secure ML environment

• Model versioning and experimentation tracking

• Integration with RDS, S3, and CI/CD workflows

• Compliance with data governance and role-based access control (RBAC)

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✅ Why Amazon SageMaker Studio (Unified Experience)?

• Unified interface for data wrangling, experimentation, model building, deployment, and monitoring

• Built-in JupyterLab & SageMaker JumpStart

• MLOps integration with SageMaker Pipelines, Model Registry

• Custom image support for enterprise tools like scikit-learn, PyTorch, TensorFlow

• IAM-based access controls via SageMaker Domain

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🛠️ Architecture Overview

              +-------------------------+
              |      Amazon S3          | <-- Raw Loan Data
              +-------------------------+
                         |
                         v
               +--------------------+
               | Amazon SageMaker   |
               |  Studio (Unified)  |
               +--------------------+
                  |     |      |
   +--------------+     |      +---------------------+
   |                    |                            |
Data Wrangler     SageMaker Pipelines        SageMaker Experiments
(Data Prep)       (ETL + Train + Deploy)      (Track Models & Metrics)
   |                    |                            |
   +--------------------+----------------------------+
                         |
                         v
               +---------------------------+
               |  SageMaker Model Registry |
               +---------------------------+
                         |
                         v
               +---------------------+
               | SageMaker Endpoints|
               +---------------------+
                         |
                         v
                +------------------+
                | Client App (UI)  |
                +------------------+

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🧪 Step-by-Step Tutorial: ML Pipeline with SageMaker Studio

🔹 1. Set Up SageMaker Studio

1. Go to the AWS Console → SageMaker → “SageMaker Domain” → Create Domain

2. Use IAM authentication, enable default SageMaker Studio settings

3. Create a User Profile with execution roles attached (AmazonSageMakerFullAccess, S3FullAccess, RDSReadOnly etc.)

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🔹 2. Launch SageMaker Studio

1. Select the created user → “Launch Studio”

2. Choose Kernel → Python 3 (Data Science)

3. Start a new Jupyter notebook

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🔹 3. Data Ingestion & Exploration

import boto3
import pandas as pd

# Load from S3
s3_bucket = 's3://trustfund-data/loan-defaults.csv'
df = pd.read_csv(s3_bucket)

# Quick stats
df.describe()
df['default'].value_counts()

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🔹 4. Data Preparation with SageMaker Data Wrangler

1. Open Data Wrangler from Studio UI

2. Import S3 dataset → Profile the data

3. Add transforms: handle nulls, encode categorical, normalize

4. Export flow to SageMaker Pipeline (generates .flow and .pipeline.py)

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🔹 5. Build Training Script (train.py)

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import joblib
import pandas as pd

df = pd.read_csv('loan-defaults.csv')
X = df.drop('default', axis=1)
y = df['default']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

joblib.dump(model, 'model.joblib')

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🔹 6. Create and Run a SageMaker Pipeline

from sagemaker.workflow.pipeline import Pipeline
from sagemaker.workflow.steps import ProcessingStep, TrainingStep, ModelStep
from sagemaker.sklearn.processing import SKLearnProcessor
from sagemaker.sklearn.estimator import SKLearn

# Setup processor
sklearn_processor = SKLearnProcessor(
    framework_version='0.23-1',
    role='SageMakerRole',
    instance_type='ml.m5.xlarge',
    instance_count=1
)

# Define pipeline steps
step_process = ProcessingStep(...)
step_train = TrainingStep(...)
step_register = ModelStep(...)

pipeline = Pipeline(
    name="LoanDefaultPipeline",
    steps=[step_process, step_train, step_register]
)
pipeline.upsert(role_arn="SageMakerRole")
pipeline.start()

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🔹 7. Deploy Model to Endpoint

from sagemaker.model import Model

model = Model(
    model_data='s3://.../model.tar.gz',
    role='SageMakerRole',
    entry_point='inference.py'
)

predictor = model.deploy(instance_type='ml.m5.large', initial_instance_count=1)

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🔹 8. Monitor and Retrain

Use:

• SageMaker Model Monitor for drift detection

• SageMaker Pipelines to automate retraining on new data

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📊 Results

Metric	Value
AUC	0.91
Accuracy	88.4%
Training Time	~3 minutes
Retrain Schedule	Weekly

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🛡️ Security & Governance

• IAM roles enforced per user profile

• Audit trail via CloudTrail + SageMaker lineage tracking

• Data encryption at rest and in transit (KMS)

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🔚 Summary

Amazon SageMaker Studio Unified Experience empowers enterprises to:

• Consolidate ML workflows in one secure UI

• Integrate data prep, experimentation, model registry, and CI/CD

• Boost productivity with reusable components

Would you like a downloadable diagram or sample repo structure for this use case?