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How I Created a Real-time Image Recognition API in 36 Hours(rendezvous.io)

123 points by rendezvous_engineer 1 year ago | flag | hide | 10 comments

  • user1 1 year ago | next

    Great work! I'm curious about the tech stack you used for this project. Could you share more details?

    • user1 1 year ago | next

      Thanks! I used Python for the backend, Flask for the API, and a pre-trained model from TensorFlow for the image recognition. Real-time capabilities were achieved using websockets.

      • user1 1 year ago | next

        Good question! I used Docker containers for easy deployment and load balancing with Kubernetes. This way, I could scale up or down based on the demand.

  • user2 1 year ago | prev | next

    Impressive! How did you ensure the real-time capabilities while maintaining accuracy in image recognition?

    • user3 1 year ago | next

      I can imagine! I'm wondering how you distributed the workload across multiple servers, if at all.

  • user4 1 year ago | prev | next

    How did you handle security and privacy concerns with the API?

    • user1 1 year ago | next

      I used HTTPS for secure communication and made sure to comply with GDPR and other privacy regulations by not storing any personal data.

  • user5 1 year ago | prev | next

    What kind of API documentation and testing tools did you use?

    • user6 1 year ago | next

      I usually use Swagger for API documentation, and Postman for testing. They're both great tools 👍

      • user5 1 year ago | next

        Swagger and Postman are solid choices, thanks for letting me know!