--- title: Charmed Kubeflow 1.8 Beta is here description: Charmed KUbeflow 1.8 Beta is here. Join our livestream to learn more about it, try it out and share your feedback with us. url: https://canonical.com/blog/kubeflow-1-8-beta?format=md --- 1. [Blog](https://canonical.com/blog) 2. Article --- [Andreea Munteanu](https://canonical.com/blog/author/munteanuandreea "More about Andreea Munteanu") 4 October 2023 # Charmed Kubeflow 1.8 Beta is here [AI/ML](https://canonical.com/blog/tag/ai-ml) [CanonicalAIRoadshow](https://canonical.com/blog/tag/canonicalairoadshow) [Kubeflow](https://canonical.com/blog/tag/kubeflow) [MLOps](https://canonical.com/blog/tag/mlops) --- Share the article Have you heard the news? Charmed Kubeflow 1.8 is available in Beta. Kubeflow is the foundation of Canonical MLOps. The latest release brings improved capabilities to personalise different components of the platform, including the images that can be used in Notebooks. We are looking for data scientists, machine learning engineers, creators and AI enthusiasts to take Charmed Kubeflow 1.8 Beta for a test drive and share their [feedback](https://discourse.charmhub.io/t/charmed-kubeflow-1-8-beta-is-here/11902/2) with us. #### Want to hear more about the Beta release? Join our livestream tomorrow [Linkedin](https://www.linkedin.com/events/7112357818921287680/comments/) [Youtube](https://www.youtube.com/watch?v=fz8NWFSk4UE) ## What’s new in Kubeflow 1.8? Kubeflow 1.8 is the latest version of the [upstream project](https://www.kubeflow.org/), scheduled to go live very soon. The [roadmap](https://github.com/kubeflow/kubeflow/blob/master/ROADMAP.md) had many improvements planned, such as: * New hyperparameter tuning algorithms * Improved security planning for future releases * A resource scheduler plugin With these new features, Kubeflow is in an even stronger position to help organisations optimise their models for different use cases including GenAI, large language models ([LLMs](https://ubuntu.com/blog/what-are-large-language-models-llms)) or predictive analytics. Besides all the features introduced in the upstream project’s version, Canonical’s Charmed Kubeflow offers dynamic enablement of the sidebar from the dashboard, as well as the opportunity to add new images in Notebooks. Additionally, Charmed Kubeflow can now be deployed in fully air-gapped environments. ## Dynamic Notebook image selector Depending on the use case, each user might need different Notebook images. Whereas Tensorflow and Pytorch are by default in the bundle, niche industries or applications would get better results if they could use their own images. This increases model performance, as well as the chance to move beyond experimentation. In the machine learning operations ([MLOps](https://ubuntu.com/blog/what-is-mlops)) space, there is a fast-growing number of Notebook images specialised in different activities. For example, [NVIDIA Nemo](https://developer.nvidia.com/nemo) is specialised in GenAI and it enables a certain category of users. Giving users the freedom to add their own images allows them to focus on the use case rather than the tooling used. However, not everyone needs all of them and adding a huge list of images by default in the platform would require too many resources. Charmed Kubeflow 1.8 has the capability to add any Notebook image and benefit from its capabilities. This enables data scientists  and machine learning engineers to focus on building machine learning models that could reach their best performance, rather than worry about tools. ## Run Kubeflow offline Depending on your company policy, computing power available and various security and compliance restrictions,  you may prefer running machine learning workflows in different environments. Especially in highly regulated industries, organisations often have a need to run in air-gapped environments. To address this, companies look for MLOps platforms that can be deployed and then  work offline, on different clouds. This should allow them to complete most of the machine learning workflow within one tool, to avoid even more time spent on connecting more of those dots. [Charmed Kubeflow](https://charmed-kubeflow.io/) is an end-to-end MLOps platform that runs on Kubernetes and  allows professionals to develop and deploy machine learning models. Once data is ingested, all activities such as training, automation, model monitoring and model serving can be performed inside the tool. From its initial design, Charmed Kubeflow could run on any cloud platform and has the ability to support various scenarios, including hybrid-cloud and multi-cloud scenarios. Charmed Kubeflow is validated and constantly tested to ensure that all capabilities work in offline environments. ## Join us live: tech talk on Charmed Kubeflow 1.8 Tomorrow,**Oct 5, 2023****, 5pm GMT**, Canonical will host a live stream about Charmed Kubeflow 1.8 Beta. Together with Noha Ihab, we will continue the tradition that started with the [previous release](https://www.youtube.com/watch?v=jRVl1ugPR08)s.  We will answer your questions and talk about: * The latest release: Kubeflow 1.8 and how our distribution handles it * Key features covered in Charmed Kubeflow 1.8 * The differences between the upstream release and Canonical’s Charmed Kubeflow The live stream will be available on both [LinkedIn](https://www.linkedin.com/events/7112357818921287680/comments/) and [Youtube](https://www.youtube.com/watch?v=fz8NWFSk4UE), so pick your platform and meet us there. ## Charmed Kubeflow 1.8 Beta: try it now #### Are you already a Charmed Kubeflow user? If you are already familiar with Charmed Kubeflow, you will only have to upgrade to the latest version. We already prepared a guide, with all the steps you need to take. Please be mindful that this is not a stable version, so there is always a risk that something might go wrong. Save your work to proceed with caution.  If you encounter any difficulties, Canonical’s MLOps team is here to hear your [feedback](https://discourse.charmhub.io/t/charmed-kubeflow-1-8-beta-is-here/11902) and help you out. Since this is a Beta version, Canonical does not recommend running or upgrading it on any production environment. #### Are you new to Charmed Kubeflow? You are a real adventurer, you can go ahead and start directly with the beta version. This might result in a few more challenges for you. For all the prerequisites, follow the [tutorial](https://charmed-kubeflow.io/docs/get-started-with-charmed-kubeflow) and please check out the section “Get started”. Shortly after you deploy and install MicroK8s and Juju, you will need to add the Kubeflow model and then make sure you have the latest version. Follow the instructions below to get this up and running: juju deploy kubeflow –channel 1.7/beta –trust Now, you can go back to the [tutorial](https://charmed-kubeflow.io/docs/get-started-with-charmed-kubeflow) to finish the configuration of Charmed Kubeflow or read the [documentation](https://charmed-kubeflow.io/docs) to learn more about it. #### Don’t be shy. Share your feedback**.** Charmed Kubeflow is an open source project that grows because of the care, time and feedback that our community gives. The latest release in beta is no exception, so if you have any feedback or questions about Charmed Kubeflow 1.8, please don’t hesitate to let us know. [Share your feedback](https://discourse.charmhub.io/t/charmed-kubeflow-1-8-beta-is-here/11902) ## Meet us at Canonical AI Roadshow Feel like talking to our team in person and sharing your feedback? Meet us at the Canonical AI Roadshow. Access key dates on the event [webpage](https://ubuntu.com/ai/roadshow). [Contact us](https://ubuntu.com/ai#get-in-touch) ## Sign up for our newsletter Get the latest Canonical news and updates in your inbox. Work email: \*I agree to receive information about Canonical's products and services. By submitting this form, I confirm that I have read and agree to [Canonical's Privacy Policy](https://canonical.com/legal/dataprivacy). Sign up ## Share on --- ## Related posts [### Canonical joins the Open Secure AI Alliance](https://canonical.com/blog/open-secure-ai-alliance) Canonical is now part of the Open Secure AI Alliance, announced by NVIDIA with partners across cloud computing, cybersecurity, enterprise software, open source foundations, and... 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