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Kubeflow is an open-source AI and machine learning platform that provides ML pipelines, hyperparameter tuning, and model serving on Kubernetes.

Kubeflow is an open-source AI platform that helps teams build, manage, and scale data, AI, and machine learning workloads on Kubernetes.

They provide portable ML workflows, interactive notebooks, distributed AI training, and scalable infrastructure for teams working across different environments.

Their standout capability is Kubeflow Trainer, which supports scalable LLM fine-tuning and model training across frameworks such as PyTorch, HuggingFace, JAX, and XGBoost.

They also offer automated machine learning, hyperparameter tuning, model management, pipelines, and a centralized dashboard through modular open-source projects.

Kubeflow is fully open source, allowing teams to use, modify, and self-host its components without a paid plan or free trial requirement.

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Free Usage Policy100% Free
Paid Upgrade Option? No
Tool Release Year2017
Founded byJeremy Lewi, Vish Kannan, and David Aronchick
EmployeesNA
LocationNA
Social media presence
Popularity Index9.7
Main FeaturesML Pipeline Orchestration, Interactive JupyterLab Notebooks, Distributed Model Training, LLM Fine-Tuning, Hyperparameter Tuning
Best Used ForKubeflow is best used by data scientists, ML engineers, MLOps teams, AI engineers, research teams, and enterprises

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Kubeflow is open source and available for free rather than being offered as a time-limited software trial.
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