Open-source machine learning toolkit built for Kubernetes, Kubeflow has now released its 1.2 version. Some of the major features in this new version include enhancements to the model building, training tuning, and machine learning pipelining; introduction of automated configuration of hyperparameters for better accuracy; serverless interfacing on Kubernetes; and providing interactive coding environment for better model development. Some of the important features are discussed below.
A Kubernetes-based system for hyperparameter tuning and neural architecture search, Katib supports several machine learning frameworks such as TensorFlow, XGBoost, and Pytorch.
The new version of Kubeflow introduces an upgraded Katib with v1bet1 AP1 for ensuring delivery of more accurate models using better infrastructure utilisation. It will be achieved through the automated configuration of hyperparameters (variables that control the model training process). A few of the important features of Katib 0.10 include:
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