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Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. A pipeline is a description of an ML workflow, including all of the components in the workflow and how they combine in the form of a graph. The pipeline includes the definition of the inputs (parameters) required to run the pipeline and the inputs and outputs of each component. A pipeline component is a self-contained set of user code, packaged as a Docker image, that performs one step in the pipeline. For example, a component can be responsible for data preprocessing, data transformation, model training, and so on.
Features
- Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK
- End to end orchestration enabling and simplifying the orchestration of end to end machine learning pipelines
- Easy experimentation making it easy for you to try numerous ideas and techniques, and manage your various trials/experiments.
- Easy re-use enabling you to re-use components and pipelines to quickly cobble together end to end solutions, without having to re-build each time.
- Documentation available
- Install Kubeflow Pipelines from choices described in Installation Options for Kubeflow Pipelines
- Kubeflow Pipelines Slack Channel
- Kubeflow Pipelines Community Meeting
Project Samples
Categories
Machine LearningLicense
Apache License V2.0Follow Kubeflow pipelines
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