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Computer Vision Pipeline

PRJ-AZURE-AI-052

Automated image classification and object detection

~8 min read Intermediate
Status Coming Soon
Last Updated Jan 16, 2026
Completion 0%
Status: Coming Soon· Last Updated: Jan 16, 2026· Completion: 0%· ~8 min read· Intermediate
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Implementation Guide

Comprehensive step-by-step deployment guide

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Estimated Monthly Cost

~$38/mo on minimal config
ComputeStorageMonitor
Business ContextManual, time-consuming visual inspection processes leading to high operational c…

The Problem

  • Manual, time-consuming visual inspection processes leading to high operational costs and human error in quality control.
  • Lack of scalable and real-time object detection capabilities for large volumes of visual data, hindering rapid decision-making.
  • Inconsistent classification accuracy across diverse image datasets due to reliance on traditional, non-AI methods.

The Solution

  • Implements Azure Custom Vision for tailored image classification models, enabling precise identification of domain-specific objects.
  • Leverages Azure Cognitive Services (specifically Computer Vision API) to perform advanced image analysis, including object detection and attribute extraction.
  • Orchestrates automated workflows using Azure Logic Apps to trigger image processing upon upload and route classification results to downstream systems.

Business Value

  • Reduces manual inspection time by 70%, reallocating human resources to higher-value tasks.
  • Increases object detection accuracy to 95% for critical assets, minimizing false positives and negatives.
  • Accelerates visual data processing throughput by 5x, supporting real-time operational insights.
  • Decreases operational costs associated with quality control and visual data analysis by 30% annually.

Risk Mitigation

  • Mitigates risks of human error and inconsistency in visual data analysis through automated, AI-driven processes.
  • Reduces compliance risks associated with data privacy by implementing secure Azure Cognitive Services endpoints.
  • Ensures business continuity by providing a scalable and resilient computer vision pipeline on Azure.
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/IEC 42001:2023 (AI Management System): Provides guidance for managing AI systems responsibly.
  • SOC 2 Type 2: Ensures security, availability, processing integrity, confidentiality, and privacy of data processed by the pipeline.

Security Controls Implemented

  • Access Control: Azure Active Directory integration for Custom Vision and Cognitive Services access.
  • Data Encryption: Data at rest encrypted using Azure Storage encryption for images and models.
  • Logging and Monitoring: Azure Monitor and Azure Log Analytics for tracking Logic Apps executions and API calls.
  • Secure Configuration: Azure Policy to enforce secure configurations for all deployed Azure resources.
  • Vulnerability Management: Regular scanning of Azure Container Registry images used by Custom Vision for vulnerabilities.

Audit Evidence

  • Azure Activity Logs: Records of all management operations and service health events.
  • Azure Diagnostic Logs: Detailed logs from Custom Vision, Cognitive Services, and Logic Apps.
  • Azure Policy Compliance Reports: Demonstrates adherence to security and configuration policies.
  • Security Center Alerts: Records of detected threats and security recommendations.

Regulatory Alignment

  • GDPR (General Data Protection Regulation) Article 5: Principles relating to processing of personal data (e.g., data minimization in image processing).
  • HIPAA (Health Insurance Portability and Accountability Act) Security Rule § 164.312: Technical safeguards for ePHI (e.g., access control for medical images).
  • California Consumer Privacy Act (CCPA) Section 1798.100: Consumer right to know about personal information collected (e.g., transparency in image data usage).

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Architecture Diagram

PRJ-AZURE-AI-052 Architecture

Technology Stack

Custom Vision
Cognitive Services
Logic Apps
Computer Vision

Complete Documentation

Prerequisites

Contributor or Owner role
Azure CLI 2.x configured
Terraform >= 1.5 (optional)
Active Azure subscription
Service Principal with RBAC
1

Clone & Authenticate

Clone the repository and authenticate with Azure CLI using your service principal or interactive login.

az login && az account set --subscription
2

Review RBAC Assignments

Review the required role assignments and ensure your identity has the correct permissions in the target resource group.

az role assignment list --assignee
3

Initialize Infrastructure

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

terraform init && terraform plan -out=tfplan
4

Deploy Resources

Apply the Terraform plan to provision all Azure resources in your target subscription.

terraform apply tfplan
5

Verify & Monitor

Verify the deployment in the Azure Portal and check Azure Monitor for any alerts or issues.

az monitor activity-log list --resource-group

Deployment Guide

Step-by-step instructions to deploy this project

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Architecture Diagram

Visual representation of the system architecture

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Source Code

Complete source code and configuration files

View on GitHub

Video Tutorial

Watch the complete walkthrough video

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