Tutorial MLOps untuk Semua (Bahasa Korea)

<p>- Panduan bagi mereka yang ingin mempelajari MLOps tetapi tidak tahu harus mulai dari mana<br /> - Dokumentasi open source (MIT) yang dapat diikuti siapa saja<br /> <br /> Introduction<br /> 1. What is MLOps?<br /> 2. Components of MLOps<br /> 3. Why Kubernetes?<br /> <br /> Setup Kubernetes <br /> 1. Introduction<br /> 2. Setup Kubernetes<br /> 3. Install Prerequisite<br /> 4.1. Install Kubernetes - K3s<br /> 4.2. Install Kubernetes - Minikube<br /> 4.3. Install Kubernetes - Kubeadm<br /> 5. Install Kubernetes Modules<br /> 6. (Optional) Setup GPU<br /> <br /> Setup Components<br /> 1. Kubeflow<br /> 2. MLflow Tracking Server<br /> 3. Seldon-Core<br /> 4. Prometheus &amp; Grafana<br /> <br /> Panduan UI Kubeflow <br /> 1. Central Dashboard<br /> 2. Notebooks<br /> 3. Tensorboards<br /> 4. Volumes<br /> 5. Experiments(AutoML)<br /> 6. Tentang Kubeflow Pipeline<br /> <br /> Kubeflow <br /> 1. Kubeflow Introduction<br /> 2. Kubeflow Concepts<br /> 3. Install Requirements<br /> 4. Component - Write<br /> 5. Pipeline - Write<br /> 6. Pipeline - Upload<br /> 7. Pipeline - Run<br /> 8. Component - InputPath/OutputPath<br /> 9. Component - Environment<br /> 10. Pipeline - Setting<br /> 11. Pipeline - Run Result<br /> 12. Component - MLFlow<br /> 13. Component - Debugging<br /> <br /> API Deployment<br /> 1. What is API Deployment?<br /> 2. Deploy SeldonDeployment<br /> 3. Seldon Monitoring<br /> 4. Seldon Fields<br /> 5. Model from MLflow<br /> 6. Multi Models<br /> <br /> - Hal-hal yang belum sempat dibahas<br /> - Instalasi virtual environment Python </p>