⚙️ Kubeflow Flashcards
🚀 What is Kubeflow?
Kubeflow is an open-source platform designed to run machine learning workflows on Kubernetes seamlessly.
🧱 What is a Kubeflow Pipeline?
Kubeflow Pipelines are reusable ML workflows defined as a sequence of steps that are containerized and run on Kubernetes.
🧪 What components are in Kubeflow?
It includes Pipelines, Notebooks, Katib (AutoML), TFJob, PyTorchJob, MPIJob, and more.
🔍 What is Katib?
Katib is Kubeflow’s component for hyperparameter tuning and automated model search (AutoML).
💻 Does Kubeflow support Jupyter Notebooks?
Yes, Kubeflow provides Notebook Servers for running Jupyter Notebooks inside the Kubernetes cluster.
⚖️ Kubeflow vs MLflow?
Kubeflow is Kubernetes-native and full stack. MLflow is language-agnostic and simpler to set up for experiment tracking.
🌐 Is Kubeflow cloud-agnostic?
Yes. Kubeflow runs on any Kubernetes cluster—on-premises or on cloud (GKE, EKS, AKS, etc.).
🔐 Does Kubeflow support RBAC?
Yes, Kubeflow supports multi-user environments with namespaces and Role-Based Access Control (RBAC).
📊 Monitoring in Kubeflow?
Monitoring is achieved via Prometheus, Grafana, and integration with tools like Kiali and Istio for service mesh observability.
🛠️ Can I use Kubeflow for training & serving?
Yes, it supports training via TFJob, MPIJob, etc., and serving via KFServing (now KServe).