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End-to-End MLOps with Pipelines

PRJ-AWS-MLS-043

Automated ML workflow orchestration

~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
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Estimated Monthly Cost

~$55/mo on minimal config
SageMaker $32Kinesis $10S3 $8CloudWatch $5
Business ContextManual inspection in manufacturing is prone to human error, leading to inconsist…

The Problem

  • Manual inspection in manufacturing is prone to human error, leading to inconsistent quality and increased recall rates.
  • Traditional machine vision systems lack adaptability to new defect types and require extensive, specialized programming for each variation.
  • High operational costs associated with manual quality control processes and the financial impact of undetected defects reaching consumers.

The Solution

  • Leveraging AWS SageMaker for building, training, and deploying custom Convolutional Neural Network (CNN) models for precise defect identification.
  • Utilizing Amazon Rekognition Custom Labels to rapidly train object detection models with minimal data, accelerating the deployment of new quality checks.
  • Employing Amazon Ground Truth for efficient and accurate labeling of industrial images, creating high-quality datasets essential for robust model training.

Business Value

  • Reduces defect escape rate by 95%, minimizing warranty claims and customer dissatisfaction.
  • Increases inspection throughput by 300%, enabling faster production lines and higher output.
  • Decreases operational costs associated with manual quality control by 40% within the first year.
  • Improves product quality consistency, enhancing brand reputation and customer loyalty.

Risk Mitigation

  • Addresses the risk of human error in quality control through automated, AI-driven inspection.
  • Mitigates the risk of slow adaptation to new defect patterns by enabling rapid model retraining with Rekognition Custom Labels.
  • Reduces financial losses from undetected defects by ensuring high accuracy and consistency in the inspection process.
  • Minimizes operational downtime by providing a scalable and resilient quality control infrastructure on AWS.
GRC MappingNIST AI Risk Management Framework (AI RMF) v1.0: Addresses trustworthy AI system…

Compliance Frameworks

  • NIST AI Risk Management Framework (AI RMF) v1.0: Addresses trustworthy AI system design and deployment.
  • ISO 42001:2023 (AI Management System): Provides guidance for managing AI systems responsibly.
  • ISA/IEC 62443 (Industrial Automation and Control Systems Security): Ensures cybersecurity for manufacturing operational technology.
  • ISO 9001 (Quality Management Systems): Supports consistent product quality and customer satisfaction.

Security Controls Implemented

  • Data encryption at rest and in transit for datasets stored in Amazon S3, used by Ground Truth and SageMaker.
  • Access control policies (IAM) restricting SageMaker notebook and Rekognition Custom Labels access to authorized personnel only.
  • Logging and monitoring of all SageMaker and Rekognition Custom Labels API calls via AWS CloudTrail and CloudWatch.
  • Secure network configurations for SageMaker endpoints, isolating them within a Virtual Private Cloud (VPC).
  • Regular security patching and vulnerability management for underlying infrastructure managed by AWS for SageMaker and Rekognition.

Audit Evidence

  • AWS CloudTrail logs detailing all SageMaker model training and deployment activities.
  • Amazon Ground Truth labeling job reports and dataset manifests.
  • AWS Config rules compliance reports for S3 bucket policies and IAM roles.
  • Model lineage documentation within SageMaker, including training data versions and model artifacts.

Regulatory Alignment

  • EU AI Act (Proposed): Article 10 (Data governance), Article 15 (Human oversight), Article 52 (Transparency obligations for high-risk AI systems).
  • FDA 21 CFR Part 11 (Electronic Records; Electronic Signatures): Relevant for data integrity and audit trails in regulated manufacturing.
  • ISO 13485 (Medical Devices Quality Management Systems): Applicable if the manufacturing process involves medical devices, focusing on quality and regulatory compliance.
  • General Data Protection Regulation (GDPR): Article 5 (Principles relating to processing of personal data), Article 32 (Security of processing) for any personal data handled during labeling.

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Architecture Diagram

PRJ-AWS-MLS-043 Architecture

Technology Stack

SageMaker Pipelines
Model Registry
EventBridge
MLOps

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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