Home Projects Architecture Case Studies AWS
Coming Soon AWS AWS Machine Learning Specialty

Model Explainability with Clarify

PRJ-AWS-MLS-045

Interpretable machine learning with SHAP

~8 min read Advanced
Status Coming Soon
Last Updated Jan 16, 2026
Completion 0%
Status: Coming Soon· Last Updated: Jan 16, 2026· Completion: 0%· ~8 min read· Advanced
Download Guide Watch Tutorial View Architecture Download Architecture

Estimated Monthly Cost

~$55/mo on minimal config
SageMaker $32Kinesis $10S3 $8CloudWatch $5
Business ContextData privacy concerns preventing centralized machine learning model training on …

The Problem

  • Data privacy concerns preventing centralized machine learning model training on sensitive datasets.
  • Operational complexities and high costs associated with transferring large volumes of data to a central location for training.
  • Inability to leverage diverse, geographically dispersed datasets due to regulatory restrictions and data sovereignty issues.

The Solution

  • Implements a Federated Learning framework using AWS SageMaker for model aggregation and management.
  • Leverages AWS Greengrass and IoT Core to securely deploy and manage local model training on edge devices or data silos.
  • Ensures data privacy by performing model training locally on decentralized datasets, sharing only model updates, not raw data.

Business Value

  • Reduces data transfer costs by 70% by eliminating the need for centralized data aggregation.
  • Accelerates model deployment cycles by 50%, enabling faster iteration and adaptation to new data patterns.
  • Increases data privacy compliance assurance, reducing potential regulatory fines by up to 90%.
  • Expands access to previously inaccessible, sensitive datasets, improving model accuracy by an average of 15%.

Risk Mitigation

  • Mitigates data breach risks by keeping sensitive data localized and transmitting only encrypted model parameters.
  • Addresses regulatory non-compliance by design, adhering to data sovereignty and privacy laws (e.g., GDPR, HIPAA).
  • Reduces single point of failure by distributing the training process across multiple nodes.
GRC MappingNIST AI Risk Management Framework (AI RMF): For managing risks associated with A…

Compliance Frameworks

  • NIST AI Risk Management Framework (AI RMF): For managing risks associated with AI systems, focusing on governance, mapping, measuring, and managing.
  • ISO/IEC 42001 (AI Management System): For establishing, implementing, maintaining, and continually improving an AI management system.
  • ISO/IEC 27001 (Information Security Management): For establishing, implementing, maintaining, and continually improving an information security management system, particularly relevant for data in transit and at rest.
  • GDPR (General Data Protection Regulation): For protecting personal data and privacy within the European Union and European Economic Area.

Security Controls Implemented

  • Data Minimization: Only model parameters, not raw data, are transmitted, reducing exposure via Federated Learning.
  • Access Control: IAM policies in AWS SageMaker restrict access to model aggregation endpoints and training jobs.
  • Encryption in Transit/Rest: TLS 1.2+ for data communication via AWS IoT Core and KMS encryption for model artifacts in S3.
  • Secure Device Management: AWS Greengrass provides secure over-the-air updates and configuration management for edge devices.
  • Logging and Monitoring: CloudWatch logs and metrics for SageMaker, Greengrass, and IoT Core activities to detect anomalies.

Audit Evidence

  • Federated Learning model update logs and aggregation reports from AWS SageMaker.
  • AWS IoT Core device connection and message routing logs.
  • AWS Greengrass component deployment and execution logs on edge devices.
  • IAM policy documents and access reviews for SageMaker and IoT Core resources.

Regulatory Alignment

  • GDPR Article 5 (Principles relating to processing of personal data): Adherence through privacy-preserving federated learning.
  • HIPAA Security Rule (45 CFR Part 164, Subpart C): Protection of electronic protected health information (ePHI) in healthcare use cases.
  • CCPA Section 1798.100 (Consumer Rights): Supports consumer rights by minimizing direct access to personal data.
  • NIST SP 800-53 Rev. 5 (Privacy Control Family): Implementation of privacy controls through data anonymization and distributed processing.

Video tutorial coming soon!

Subscribe to our YouTube channel to get notified when this tutorial is published.

Subscribe on YouTube

Architecture Diagram

PRJ-AWS-MLS-045 Architecture

Technology Stack

SageMaker Clarify
SHAP
QuickSight
Explainability

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

Download Architecture

Source Code

Complete source code and configuration files

View on GitHub

Video Tutorial

Watch the complete walkthrough video

Watch Now