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Hybrid Cloud for Image Analysis: Maximizing Efficiency with On-Prem Processing and Cloud AI

Category:
AI / Cloud

The Problem Statement

The client currently operates image stitching and grading modules on an on-premises machine, requiring manual intervention to initiate processing whenever new images arrive. This leads to inefficiencies, delays, and increased operational overheads. Furthermore, the system lacks automation, scalability, and seamless integration for AI/ML-based grading and end-user review. 

To address these challenges, a fully automated solution must be developed by leveraging cloud services for scalability and security while retaining critical components on-premises for local processing. The enhanced system will: 

  • Automate the image stitching process, removing the need for manual triggers. 
  • Leverage AI/ML models for grading stitched images and extracting insights. 
  • Enable seamless deployment of both on-premises and cloud components. 
  • Provide a web-based platform for reviewers to access, analyze, and manage processed images. 
  • Ensure secure and efficient communication between on-premises and cloud systems while maintaining performance. 

The Solution

To achieve a seamless and efficient image processing workflow, a hybrid solution will be designed, combining on-premises infrastructure with cloud capabilities. This approach will automate the image processing pipeline, eliminating manual interventions and reducing delays. By leveraging cloud-based AI/ML models, the system will enhance scalability and enable intelligent grading of processed images. Additionally, a web-based interface will be implemented to streamline the review process, allowing users to access and analyze images efficiently. The solution will ensure seamless integration between on-premises and cloud components, optimizing performance while maintaining security and operational flexibility. 

Architecture

  1. On-Premises Components 
    1. The existing image stitching module will remain on-premises for local processing. 
    2. A local monitoring service will detect new images and trigger automatic stitching. 
    3. Secure connectivity will be established to upload stitched images to the cloud. 
  2. Cloud Components 
    1. Stitched images will be stored in a cloud storage service (Azure Blob) 
    2. The AI/ML-based image grading module will run in the cloud. 
    3. A web application will be deployed for reviewers to access processed images. 
    4. Cloud orchestration will manage the workflow between on-premises and cloud components. 

Automation & Data Flow

  1. Automated Image Stitching (Cloud) 
    1. A local service (Python script) monitors a specified folder for new images. 
    2. When new images are detected, the image processing module is triggered automatically. 
    3. The stitched image is saved in the Azure public output folder. 
  2. Uploading to Cloud 
    1. Once stitching is completed, the local service uploads the processed image to cloud storage. 
    2. Secure API calls for the image processing module 
  3. AI/ML-Based Image Grading (Cloud) 
    1. A cloud function (Azure Functions) triggers AI/ML-based grading when new images arrive in storage. 
    2. AI/ML models deployed on Azure ML analyze the images and generate grading results. 
    3. The results are stored in a database (PostgreSQL). 
  4. Web-Based Review System 
    1. React.js will provide an interface for reviewers. 
    2. The web app fetches processed images and grading results via REST API. 
    3. Users can filter, analyze, and review the images with AI-generated insights. 
  5. Deployment & Monitoring 
    1. Azure ML Ops for deployment and monitoring. 
    2. Cloud: Use serverless functions for auto-scaling. 
    3. Monitoring: Set up logging and alerts using Azure Monitor and Azure Functions logs. 

Value Add Delivered

  1. Automation: Eliminates manual intervention for image stitching and grading. 
  2. Scalability: AI-based grading scales automatically with image volume. 
  3. Security: Encrypted data transfer and RBAC for secure access. 
  4. Efficiency: Reduces processing time and speeds up image review. 
  5. Hybrid Flexibility: Retains necessary on-prem processing while leveraging cloud benefits.