Build your machine learning pipeline with Kubeflow

Join our technical demo where Andreea Munteanu, AI/ML Product Manager and Kimonas Sotirchos, Kubeflow Software Engineer, will: * Talk about key considerations when building machine learning pipelines * Learn how to build ML pipelines using Kubeflow * Give insight of some best practices when using Kubeflow pipelines More information about the webinar: Machine Learning Operations is a new practice that ensures machine learning projects run in an automated and reproducible manner. It is often described as DevOps for machine learning, since it brings together ML development and the operations that are required afterwards to maintain any ML initiative. MLOps has three core components: data pipelines, model pipelines and applications pipelines, which developers are often looking to optimize. What are ML pipelines? ML pipelines refer to the pipelines of all three components of the machine learning lifecycle and enable developers to automate ML workloads, streamlining the process of taking models to production. They are a foundational aspect to scaling machine learning projects and running them in a more effective manner. There are multiple ways to build ML pipelines. Open source tools such as Kubeflow are an easy option, as they have already built the engine to perform such tasks. Kubeflow Pipelines are one of Kubeflow’s components which can be used to build ML pipelines. Learn more or contact our team: https://canonical.com/solutions/ai