Kubeflow 1.4: Machine Learning on Kubernetes

Introduction

Kubeflow 1.4 — Machine Learning on Kubernetes — was released on September 15, 2021.

This is a practical update aimed at making day‑to‑day Kubernetes work a bit more predictable.

In this release: Kubeflow 1.4 enhances machine learning capabilities with improved pipelines, better model serving, and enhanced integration for MLOps on Kubernetes.

Pipeline Improvements

• Pipeline execution optimizations reduce time required for ML pipeline runs.
• Pipeline scheduling enhancements enable more flexible execution patterns.
• Pipeline versioning improvements enable better management of pipeline changes.
• Pipeline monitoring expansion provides better visibility into pipeline execution.

Model Serving

1. Serving performance optimizations reduce latency for model inference.
2. Multi-model support enables serving multiple models from a single deployment.
3. Auto-scaling improvements provide better resource utilization for serving workloads.
4. A/B testing support enables gradual rollout of new model versions.

Integration Enhancements

• Notebook improvements provide better Jupyter notebook integration.
• Training enhancements simplify distributed training workflows.
• Hyperparameter tuning improvements enable more efficient model optimization.
• Experiment tracking expansion provides better ML experiment management.

Operational Features

• Resource management improvements enable better GPU and CPU allocation.
• Monitoring expansion includes ML-specific metrics and health indicators.
• Security enhancements provide better isolation for ML workloads.
• Documentation improvements provide clearer guides for common ML workflows.

Getting Started

kubectl apply -k "github.com/kubeflow/manifests/kubeflow-1.4.0?ref=v1.4.0"

Summary

Kubeflow 1.4 continues to evolve as a leading MLOps platform, providing teams with tools to build, train, and deploy ML models on Kubernetes.