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I’ve finished training a convolutional neural network and now I want it running reliably on an AWS EC2 instance. I already have an AWS account and am settled on using EC2 rather than SageMaker or Lambda, so the task is purely about standing up the production environment and proving that the model answers live requests. Here’s what I need: • Spin up and configure an EC2 instance (Ubuntu preferred) with GPU drivers, CUDA / cuDNN, Python, and either TensorFlow or PyTorch—whichever my model requires. • Package the model (saved .h5 or .pt plus any preprocessing code) into a lightweight service—Flask, FastAPI, or another simple REST interface is fine. • Expose a secure HTTPS endpoint behind an AWS load balancer or an Nginx reverse proxy so I can hit /predict with input data and get the JSON response. • Supply a short README noting instance type, AMI, commands used for setup, environment variables, and a quick test script that proves end-to-end inference works. Acceptance criteria The endpoint returns a correct prediction on the provided sample image within reasonable latency, the service autostarts on reboot, and all steps are reproducible from your documentation.
Proje No: 40073847
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Hello, As a Full-Stack AI & Web Developer, my expertise lies in building scalable and high-performance applications, which make me a perfect fit for your project. I have extensive knowledge and experience in Python - the core language needed for your task, and deep knowledge of AWS services including EC2, Ubuntu, and Nginx. Moreover, I'm well-versed in deploying deep learning models using TensorFlow or PyTorch alongside Flask or FastAPI. Having built numerous RESTful interfaces and dealing with large datasets during my career, I understand the importance of lightweight deployments that don't compromise on performance. I will ensure that your model is efficiently packaged into a service with all the necessary drivers and dependencies. My proficiency extends beyond just creating the service – I grasp the significance of security as well. So, count on me to set up HTTPS endpoint behind an AWS load balancer or an Nginx reverse proxy to secure data transmission. Lastly, my strong commitment to documenting my work thoroughly will guarantee that all steps involved in this deployment process are easily reproducible from your documentation. Plus, I'll supply a comprehensive README file including AMI details, instance type used, setup commands, pre-defined environment variables and a test script with a sample image to verify the end-to-end functioning of your model. Choose me for this task and get ready for a reliable and hassle-free deployment! Thanks!
₹1.165 INR 3 gün içinde
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4 freelancer bu proje için ortalama ₹1.291 INR teklif veriyor

Hi, how are you doing? I have solid hands-on experience deploying ML workloads on AWS EC2 with GPU setups, CUDA/cuDNN, and Python-based APIs like Flask or FastAPI, plus packaging models (.h5/.pt) for production. I’ve done end-to-end deployments with HTTPS endpoints behind a load balancer or Nginx, plus auto-start on reboot and concise, reproducible READMEs. I can spin up the instance, configure the stack, wire a quick /predict endpoint, and deliver a compact test script and docs to prove live inference. Let me know if you want me to share relevant prior work to review.
₹1.500 INR 5 gün içinde
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