What's inside Kubeflow?
Multi-user dashboard with role-based access control (RBAC) allows data scientists to focus on creating great models, while the ops team takes care of the backend.
Pipelines map dependencies between components in the ML workflow where each component is a containerised piece of ML code. Learn more ›
ML libraries & frameworks
Every time you run a Kubeflow pipeline with specific parameters the results of this run are stored so they can be easily compared and replicated.
Kubeflow includes Katib for hyperparameter tuning. Katib runs pipelines with different hyperparameters (e.g. learning rate, # of hidden layers) optimizing for the best ML model.
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, Hidden Technical Debt in Machine Learning Systems, 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.
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.