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An end-to-end ML model deployment pipeline on GCP: train in Cloud Shell, containerize with Docker, push to Artifact Registry, deploy on GKE, and build a basic frontend to interact through exposed endpoints. This showcases the benefits of containerized deployments, centralized image management, and automated orchestration using GCP tools.

Home Page: https://medium.com/@shreyjain203/deploying-a-machine-learning-model-to-production-website-using-containers-gke-a-step-by-step-d2f17cc30643

Dockerfile 13.67% Python 45.39% HTML 40.94%
artifact-registry docker-container docker-image google-cloud-platform google-kubernetes-engine kubernetes-deployment machine-learning-deployment ml-deployment

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