Kubeflow. MLOps open-source tool by Google on top of Kubernetes
Almost immediately after Kubernetes established itself as the standard for working with a cluster of containers, Google created Kubeflow—an open-source project that simplifies working with ML in Kubernetes. It has all the advantages of this orchestration tool, from the ability to deploy on any infrastructure to managing loosely-coupled microservices, and on-demand scaling.
This project is for developers who want to deploy portable and scalable machine learning projects. Google didn’t want to recreate other services. They wanted to create a state-of-the-art open-source system that can be applied alongside various infrastructures—from supercomputers to laptops.
With Kuberflow, you can benefit from the following features:
- Jupyter notebooks
Create and customize Jupyter notebooks, immediately see the results of running your code, create interactive analytics reports.
- Custom TensorFlow job operator
This functionality helps train your model, and apply a TensorFlow or Seldon Core serving container to export the model to Kubernetes.
- Simplified containerization
Kuberflow eliminates the complexity involved in containerizing the code. Data scientists can perform data preparation, training, and deployment in less time.
All in all, Kuberflow is a full-fledged solution for the development and deployment of end-to-end ML workflows.
️