What is Kubeflow?

Kubeflow is the open source machine learning toolkit on top of Kubernetes.

Kubeflow translates steps in your data science workflow into Kubernetes jobs, providing the cloud-native interface for your ML libraries, frameworks, pipelines and notebooks.

Contributors to Kubeflow

What's inside Kubeflow?

Kubeflow dashboard

with provides a platform for data scientists and engineers to leverage K8s to develop, deploy and monitor their models in production.

Jupyter, VSCode and RStudio

After Kubeflow v1.3 users can but also RStudio or VSCode directly from the dashboard, allocating the right storage, CPUs and GPUs.

ML libraries & frameworks

Kubeflow is compatible with your choice of data science libraries and frameworks. , , , , , and more.

Kubeflow Pipelines

Automate your ML workflow into pipelines by containerizing steps as pipeline components and defining inputs, outputs, parameters and generated artifacts. Learn more ›

Experiments, Runs and Recurring Runs

Experiments, groups of and , allow you to find the right parameters for your model, compare and replicate results.

Hyperparameter tuning / AutoML

Kubeflow includes for hyperparameter tuning. Katib runs pipelines with different hyperparameters (e.g. learning rate, # of hidden layers) optimizing for the best ML model.

KFServing for inference serving

is a multi-framework model deployment tool with fun88体育less inferencing, canary roll-outs, pre & post-processing and explainability. Learn more ›

The integrations you need

Kubeflow integrates with for model registry, staging, and monitoring in production, for feature store capabilities, and for data versioning.


Save, compare and share generated artifacts - models, images, plots. Serve your models with a , including .

Easy Kubeflow operations

Charmed Kubeflow is a composable bundle of Kubeflow applications, packaged into K8s operators and pre-integrated for you.

Deploy 30+ apps that make up Kubeflow, integrate with ecosystem operators to extend functionality, and upgrade on-demand.

Why MLOps?

Bringing AI solutions to market can involve many steps: data pre-processing, training, model deployment or inference serving at scale... The list of tasks is complex and keeping them in a set of notebooks or scripts is hard to maintain, share and collaborate on, leading to inefficient processes.

In the study, , Google describes that only about 20% of the effort and code required to bring AI systems to production is the development of ML code, while the remaining is operations. Standardizing ops in your ML workflows can hence greatly decrease time-to-market and costs for your AI solutions.

Area = effort & code

Who uses Kubeflow?

Thousands of companies have chosen Kubeflow for their AI/ML stack.

From research institutions like CERN, to transport and logistics companies - Uber, Lyft, GoJek - to financial and media industries with Spotify, Bloomberg, Shopify and PayPal.

Forward-looking enterprises are using Kubeflow to empower their data scientists.

Get started today

Try-out Kubeflow on your K8s deployment. Or on MicroK8s - Zero-ops Kubernetes with high availability.

Single-command deploy locally on your desktop, public cloud VM or on-prem fun88体育.