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From Prototype to Production - Local Kubernetes: Our AI Resume Matcher's Journey
- The article discusses the transition of an AI Resume Matcher from a prototype to production using Local Kubernetes.
- Key learnings include containerizing the Java Spring Boot application with Docker, setting up a local Kubernetes environment, crafting Kubernetes deployment manifests, configuring Google Cloud authentication for Vertex AI, and exposing the application through a Kubernetes Service.
- Local Kubernetes offers advantages such as mimicking production environments, handling growth, building cloud-ready skills, and ensuring standardized deployments.
- Prerequisites include having the AI Resume Matcher project code, Docker Desktop for local Kubernetes, and a Google Cloud account with Vertex AI enabled.
- The process involves containerizing the application, launching Kubernetes with Docker Desktop, orchestrating with Kubernetes using manifest files, setting up Google Cloud authentication securely, and deploying the application.
- For Google Cloud authentication, a Service Account and JSON key file are utilized to create a Kubernetes Secret for secure authentication within the Kubernetes pod.
- Testing the deployed application involves checking pod status, verifying deployment, inspecting application logs, and sending a test request to ensure proper functioning.
- By following the steps outlined, developers can deploy the AI Resume Matcher on a local Kubernetes cluster, gaining practical experience with DevOps practices and cloud-native technologies.
- The journey from a Docker Compose setup to a Kubernetes cluster deployment enhances the application's robustness and scalability, paving the way for production readiness.
- Overall, the article provides a comprehensive guide to transitioning an AI application from a basic prototype to a fully orchestrated production environment while showcasing key DevOps principles.
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