What is Kubeflow?
Kubeflow streamlines the deployment and management of artificial intelligence and machine learning processes on Kubernetes, enhancing portability and scalability. It provides an integrated ecosystem of Kubernetes-native components designed to support each phase of the AI/ML lifecycle, incorporating best-in-class open-source tools and frameworks.
This platform facilitates running ML workflows anywhere Kubernetes is available. Key components include Pipelines for workflow orchestration, Notebooks for web-based development environments, a Central Dashboard for unified access, Trainer for model training and fine-tuning (including LLMs), Katib for AutoML tasks like hyperparameter tuning, and KServe for production model serving across various frameworks.
This platform facilitates running ML workflows anywhere Kubernetes is available. Key components include Pipelines for workflow orchestration, Notebooks for web-based development environments, a Central Dashboard for unified access, Trainer for model training and fine-tuning (including LLMs), Katib for AutoML tasks like hyperparameter tuning, and KServe for production model serving across various frameworks.
Features
- Kubeflow Pipelines (KFP): Platform for building and deploying portable, scalable ML workflows.
- Kubeflow Notebooks: Run web-based development environments (like Jupyter) on Kubernetes.
- Kubeflow Central Dashboard: Unified hub for accessing Kubeflow components and interfaces.
- Kubeflow Trainer: Kubernetes-native tool for scalable, distributed model training and LLM fine-tuning (supports PyTorch, JAX, TensorFlow, etc.).
- Katib (AutoML): Automated machine learning including hyperparameter tuning, early stopping, and neural architecture search.
- KServe (Model Serving): Production-grade model serving on Kubernetes for various frameworks (TensorFlow, XGBoost, ScikitLearn, PyTorch, ONNX).
- Kubernetes Native: Designed to run seamlessly on any Kubernetes cluster.
Use Cases
- Deploying machine learning models on Kubernetes.
- Building scalable ML pipelines.
- Managing the end-to-end ML lifecycle.
- Running interactive ML development environments.
- Automating hyperparameter tuning and model selection.
- Serving trained models in production environments.
- Fine-tuning Large Language Models (LLMs).
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