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Announcing Charmed Kubeflow 1.10
- Canonical has released Charmed Kubeflow 1.10, offering significant improvements and new capabilities for enterprise deployments.
- Highlights from upstream Kubeflow 1.10 include advanced hyperparameter tuning with Trainer 2.0 and Katib, improved scalability in Kubeflow Pipelines, and next-level model serving with KServe.
- KServe introduces features like a new Python SDK, OCI storage integration, and model caching for rapid deployment.
- Collaborations with projects like vLLM, Kubernetes WG Serving, and Envoy are addressing challenges in serving large language models.
- The integration of Feast with Kubeflow aims to enhance end-to-end MLOps experiences, especially for Generative AI and RAG use cases.
- Charmed Kubeflow 1.10 adds value with capabilities like managing profiles via GitOps, enabling Istio ingress high availability, and enhancing monitoring and security measures.
- Canonical's AI/ML ecosystem supports open source technology on any Kubernetes distribution and aims to offer a managed Kubeflow offering in the public cloud.
- Data scientists can leverage the Data Science Stack on Ubuntu for experimentation and a standalone model-serving solution is being developed for secure deployments even at the edge.
- To experience the enhancements of Charmed Kubeflow 1.10, users are encouraged to install it and explore the release notes for detailed instructions.
- For enterprise support or managed services related to Charmed Kubeflow, users can contact Canonical.
- Canonical's AI solutions can be explored further at canonical.com/solutions/ai.
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