--- title: Charmed Kubeflow 1.6 is now available from Canonical description: Canonical announces today the release of Charmed Kubeflow 1.6, an end-to-end MLOps platform with optimised complex model training capabilities. url: https://canonical.com/blog/charmed-kubeflow-1-6-available?format=md --- 1. [Blog](https://canonical.com/blog) 2. Article --- [Canonical](https://canonical.com/blog/author/canonical "More about Canonical") 8 September 2022 # Charmed Kubeflow 1.6 is now available from Canonical [AI/ML](https://canonical.com/blog/tag/ai-ml) [artificial intelligence](https://canonical.com/blog/tag/artificial-intelligence) [Kubeflow](https://canonical.com/blog/tag/kubeflow) [ML](https://canonical.com/blog/tag/ml) [MLOps](https://canonical.com/blog/tag/mlops) --- Share the article #### The latest release of Canonical’s end-to-end MLOps platform brings advanced AI/ML training capabilities **8 September 2022-** Canonical, the publisher of Ubuntu, announces today the release of [Charmed Kubeflow](https://charmed-kubeflow.io/) 1.6,  an end-to-end MLOps platform with optimised complex model training capabilities.  Charmed Kubeflow is Canonical’s enterprise-ready distribution of Kubeflow, an open-source machine learning toolkit designed for use with Kubernetes. Charmed Kubeflow 1.6 follows the same release cadence as the Kubeflow upstream project. This latest version follows the Kubeflow [roadmap](https://github.com/kubeflow/kubeflow/blob/master/ROADMAP.md) and comes with performance enhancements and more advanced model training capabilities. ### Speed up digital transformation: take models from concept to production Charmed Kubeflow helps data science teams automate tasks and boost productivity,  helping companies lower costs. The platform’s components use [charms](https://charmhub.io/kubeflow) – Kubernetes operators that automate maintenance and security operations. Charms accelerate workload deployment,  allowing data scientists to take models to market more efficiently. According to a 2022 [IBM Index AI Report](https://www.ibm.com/downloads/cas/GVAGA3JP),  adoption of AI/ML within enterprises reached 35% last year. The benefits are clear. For instance, banks [applying ML](https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/an-executives-guide-to-machine-learning) to build recommendation earnings have boosted sales by [10%](https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/an-executives-guide-to-machine-learning), saved 20% in CAPEX, increased cash collections by 20%, and reduced churn by 20%.  Identifying tools that bring automation to data science workflows has become essential to getting a faster return on investment from AI/ML projects. ### Improved data processing and tracking with MLOps Besides accelerating deployments, Charmed Kubeflow 1.6 makes data processing more seamless.  Kubeflow’s 2022 [survey](https://15905978862850875460.googlegroups.com/attach/150ca032927e6/'22%20Kubeflow%201.6%20User%20Survey%20-%20initial%20results.pdf?part=0.1.1&view=1&vt=ANaJVrEcXCrysJfHF8l7OTYMuj9QpFuRzPm8ScV-8nG3GO8seXTjCZAT3A-GeAvva11DdqJ-wLhtL8y0AEoRMEDdIh2LtzQ-zCBG1tNnMYkYtpBLmM6iq4Y) reveals that data processing and transformation are the most challenging and time-consuming activities for enterprises. Moreover, data comes from various sources, each with particular processes and dependencies. Charmed Kubeflow 1.6 comes with better tracking capabilities.   AI/ML models can be measured more effectively;  evolution and debugging become simpler. The solution detects data drift and allows models to adapt to it quickly. Charmed Kubeflow 1.6 brings improved tracking for trial logs as well, allowing for efficient debugging in case of data source failure. ### Optimised AI training and modelling Models need up to 15 iterations to become production-ready, and only half of them get that far into the AI/ML journey. Charmed Kubeflow 1.6  supports population-based training (PBT), accelerating model iteration and improving the likelihood that models will reach production readiness. An MPI operator makes training large volumes of data more efficient. PyTorch elastic training enhancements make model training more effective and help ML engineers get started quickly. ### Get a technical deep dive into Charmed Kubeflow 1.6 Canonical will be hosting a livestream on 8 September 2022 to talk more about Charmed Kubeflow 1.6. This will be a technical deep dive on some of the topics included in upstream Kubeflow’s and Canonical’s roadmap. The livestream will cover: * The new release * The importance of being part of an open source community * The challenges involved in developing an open-source product * The differences between the upstream release and Canonical’s Charmed Kubeflow The upcoming webcast will delve deeper into the features discussed in the Beta release’s livestream, which is available on YouTube. Save the event on [Facebook](https://www.facebook.com/events/1667545020297563/?ref=newsfeed) or [Linkedin](https://www.linkedin.com/events/atechnicaldeepdiveintokubeflow16970762290551767040/comments/) to get notifications about Canonical’s future events. ## 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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