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Kubeflow 1.1 is out!

New release, increased capabilities.

After 6 months since the release of 1.0, Kubeflow releases a new version with increased capabilities.

This new version has focused on improving ML Workflow Productivity, Isolation and Security, and GitOps. Here is a list of the enhanced features:

  1. Fairing and Kale (Kubeflow Automated pipeLines Engine) for end-to-end workflows.
  2. Katib hyperparameter tuning:
    1. New frameworks & algorithms (goptuna with CMA-ES, DARTS, chocolate, hyperopt, skopt).
    2. Flexible config & tuning options (new python SDK, experiments with undefined goal, new UI, new resume policy).
  3. Install and operations to support GitOps, using blueprints and kpt primitives.
  4. Isolation and security: multi-user Kubeflow pipelines, CVE scanning, and support for Google’s Private GKE and Anthos.
  5. MXNet and XGBoost distributed training operators, simplify training on multiple nodes, and speeds model creation.
  6. Seldon Core 1.1

What’s next:

  • Read the official release blog post here.
  • Suggest new features for version 1.2 on this thread!

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