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Complete AWS AWS ML Engineer - Associate

Customer Churn Prediction Platform

PRJ-AWS-MLE-003

Predictive analytics with Feature Store, Autopilot, and explainable AI using SHAP values

~8 min read Intermediate
Status Completed
Last Updated Feb 18, 2026
Completion 100%
Status: Completed· Last Updated: Feb 18, 2026· Completion: 100%· ~8 min read· Intermediate
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Estimated Monthly Cost

~$38/mo on minimal config
SageMaker $22Lambda $4S3 $8CloudWatch $4
Business ContextCompanies struggle with high customer churn rates due to an inability to proacti…

The Problem

  • Companies struggle with high customer churn rates due to an inability to proactively identify at-risk customers.
  • Traditional churn prediction models often lack transparency, making it difficult for business stakeholders to understand the drivers behind predictions.
  • Manual feature engineering and model tuning processes are time-consuming and require specialized data science expertise, slowing down deployment.

The Solution

  • Implements a robust customer churn prediction platform leveraging AWS SageMaker for end-to-end machine learning lifecycle management.
  • Utilizes AWS Feature Store to manage, discover, and share machine learning features, ensuring consistency and reducing data preparation time.
  • Deploys AWS SageMaker Autopilot to automatically build, train, and tune the best machine learning models for churn prediction.
  • Incorporates SHAP (SHapley Additive exPlanations) values to provide explainable AI, enhancing model interpretability for business users.

Business Value

  • Reduces customer churn by 15% within the first six months of deployment, directly impacting revenue retention.
  • Decreases the time-to-insight for churn prediction from weeks to hours, enabling rapid business interventions.
  • Improves the accuracy of churn predictions by 10% compared to previous methods, leading to more effective retention campaigns.
  • Lowers operational costs associated with manual model development and maintenance by 25% through automation.

Risk Mitigation

  • Mitigates the risk of undetected customer attrition by providing early warning signals.
  • Addresses the risk of biased or uninterpretable models through the use of explainable AI techniques like SHAP.
  • Reduces the risk of data inconsistencies and feature drift by centralizing feature management with AWS Feature Store.
  • Minimizes the risk of human error in model selection and hyperparameter tuning through AWS SageMaker Autopilot.
GRC MappingNIST AI Risk Management Framework (AI RMF): Addresses responsible development an…

Compliance Frameworks

  • NIST AI Risk Management Framework (AI RMF): Addresses responsible development and use of AI, particularly for explainability and fairness in predictive models.
  • ISO 42001 (AI Management System): Provides guidance for establishing, implementing, maintaining, and continually improving an AI management system.
  • GDPR (General Data Protection Regulation): Ensures protection of personal data used in predictive analytics, especially regarding data minimization and transparency.
  • AWS Well-Architected Framework (Machine Learning Lens): Guides best practices for building secure, reliable, efficient, and cost-effective ML workloads on AWS.

Security Controls Implemented

  • Data encryption at rest and in transit for all data stored in AWS Feature Store and S3 buckets used by SageMaker.
  • Access control policies (IAM) restricting SageMaker notebook and model endpoint access to authorized personnel only.
  • Network isolation for SageMaker training jobs and inference endpoints using Amazon VPC.
  • Regular security patching and vulnerability management for underlying SageMaker infrastructure.
  • Logging and monitoring of all SageMaker API calls and data access events using AWS CloudTrail and CloudWatch.

Audit Evidence

  • AWS CloudTrail logs detailing all API calls made to SageMaker, Feature Store, and other integrated services.
  • AWS Config rules demonstrating compliance with security best practices and regulatory requirements for ML workloads.
  • Model lineage and versioning records maintained within SageMaker, showing model development and deployment history.
  • SHAP value reports and model explainability dashboards providing insights into model predictions and feature importance.

Regulatory Alignment

  • GDPR Article 5 (Principles relating to processing of personal data): Adherence to data minimization, storage limitation, and integrity principles.
  • GDPR Article 22 (Automated individual decision-making, including profiling): Provision of meaningful information about the logic involved in churn predictions.
  • CCPA Section 1798.100 (Consumer Rights): Support for consumer rights regarding personal information collected and used for predictive analytics.
  • HIPAA Security Rule (45 CFR Part 164, Subpart C): If handling protected health information, ensures confidentiality, integrity, and availability of ePHI.

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Architecture Diagram

PRJ-AWS-MLE-003 Architecture

Technology Stack

Feature Store
Autopilot
Clarify
QuickSight
SHAP

Complete Documentation

Prerequisites

IAM Admin or PowerUser role
AWS CLI v2 configured
Terraform >= 1.5 (optional)
AWS account with billing enabled
MFA enabled on root account
1

Clone & Configure

Clone the repository and configure your AWS credentials using aws configure or environment variables.

aws configure --profile cloudguard
2

Review IAM Policies

Review and attach the required IAM policies to your deployment role. Ensure least-privilege access is applied.

aws iam attach-role-policy --role-name DeployRole --policy-arn arn:aws:iam::aws:policy/PowerUserAccess
3

Initialize Infrastructure

Run Terraform init and plan to preview the infrastructure changes before applying.

terraform init && terraform plan -out=tfplan
4

Deploy Resources

Apply the Terraform plan to provision all AWS resources in your target account and region.

terraform apply tfplan
5

Verify & Monitor

Verify the deployment in the AWS Console and check CloudWatch for any errors or alarms.

aws cloudwatch describe-alarms --state-value ALARM

Deployment Guide

Step-by-step instructions to deploy this project

Download Guide

Architecture Diagram

Visual representation of the system architecture

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Source Code

Complete source code and configuration files

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Video Tutorial

Watch the complete walkthrough video

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