Kubeflow 1.9: ML Platform and MLOps Enhancements

Introduction

Kubeflow 1.9, released on August 25, 2024, advances machine learning on Kubernetes with improved ML workflow capabilities, enhanced model serving, better integration, and expanded features. This release makes Kubeflow more powerful for building and deploying ML models.

ML Workflow Improvements

• Pipeline enhancements provide better ML pipeline capabilities.
• Training improvements enable better model training workflows.
• Experimentation enhancements provide better experiment tracking.
• Reproducibility improvements enable better model reproducibility.

Model Serving Enhancements

1. Serving performance improvements optimize model inference performance.
2. Multi-framework support expands support for more ML frameworks.
3. A/B testing capabilities enable model A/B testing.
4. Canary deployments enable gradual model rollouts.

Integration Improvements

• Kubernetes integration improvements provide seamless integration with Kubernetes.
• Storage integration enables integration with various storage solutions.
• Monitoring integration provides visibility into ML metrics.
• CI/CD integration enables integration with CI/CD pipelines.

Feature Expansion

• AutoML support enables automated machine learning capabilities.
• Feature store integration enables integration with feature stores.
• Model registry improvements provide better model management.
• Data versioning enables data versioning for ML workflows.

Getting Started

# Install Kubeflow
kubectl apply -k "github.com/kubeflow/manifests/kfdef/instance/generic"

Summary

Kubeflow 1.9 continues to evolve as a leading ML platform, providing teams with powerful capabilities for building and deploying ML models on Kubernetes.