In an era marked by groundbreaking technologies, Artificial Intelligence stands out as a field where Cloud Vendors are investing extensively. Azure AI Services, including Cognitive Services, are at the forefront, offering a range of accessible products for both novice and experienced users in development and production environments.
Our focus today is on exploring Azure AI Vision with the integration of the Computer Vision API in a web application for object detection. This project expands to the use of Azure Container Registry for Web Apps and involves building a Python application with Flask, containerizing it with Docker, and configuring Continuous Deployment with Webhooks.
This project involves a deployment setup using Terraform. Here's a brief overview of the deployment process:
- Code Editor: We are using VSCode for our development.
- Standard Files: The necessary files for deployment, focusing on Infrastructure as Code (IaC) principles.
main.tf: This file is the core of our Terraform configuration.- Reference Blog: For more detailed insights, check out our blog post "AZURE VISION AI – OBJECT DETECTION WEB APP WITH DOCKER AND CONTAINER REGISTRY" at CloudBlogger.
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Set Up Your Environment: Ensure you have VSCode and other required tools installed.
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Configuration: Review and modify the
main.tffile as per your project requirements. -
Deployment: Use Terraform commands to initialize, plan, and apply your configuration to deploy the resources.
To get started with this project:
- Clone the repository to your local machine.
- Install the necessary dependencies and tools.
- Follow the deployment steps outlined above.
Contributions are welcome! If you have suggestions or improvements, feel free to fork the repository, make your changes, and submit a pull request.
Follow the Blog for Detailed Instructions: For step-by-step guidance, visit Azure Vision AI – Object Detection Web App with Docker and Container Registry.
