Kubeflow 1.9: New Tools for Model Management and Training Optimization

Model Registry Fine-Tune APIs for LLMs Pipelines v1 Feature Parity Argo Workflows and Tekton Backends Consolidation Argo Workflows Upgrade Katib Central Dashboard Notebooks Kubeflow Platform (Security and Manifests) Security Manifests KServe Documentation Honorable Mentions Google Spark Operator migration to Kubeflow Google Summer of Code What’s next How to get started with 1.9 Join the Community Want to help? Kubeflow 1.9 significantly simplifies the development, tuning and management of secure machine learning models and LLMs. Highlights include: Model Registry : Centralized management for ML models, versions, and artifacts. Fine-Tune APIs for LLMs : Simplifies fine-tuning of LLMs with custom datasets. Pipelines : Consolidation of Tekton and Argo Workflows backends for improved flexibility. Security Enhancements : Network policies, Oauth2-proxy, and CVE scanning. Integration Upgrades : Improved integrations with Ray, Seldon, BentoML, and KServe for LLM GPU optimizations. Installation and Documentation : Streamlined installation, updated platform dependencies, and enhanced documentation. These updates aim to simplify workflows, improve integration dependencies, and provide Kubernetes-native operational efficiencies for enterprise scale, security, and isolation. A model registry provides a central catalog for ML model developers to index and manage models, versions, and ML artifacts metadata. It fills a gap between model experimentation and production activities. It provides a central interface for all stakeholders in the ML lifecycle to collaborate on ML models. Model registry has been asked by the community for a long time and we are delighted to introduce it to the Kubeflow ecosystem.