Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore MLFlow, an open source platform for managing machine learning workflows, in this 46-minute webinar presented by Andreea Munteanu from Canonical Ubuntu. Gain insights into MLFlow's position in the machine learning landscape, its benefits, and its four main components: experiment tracking, model registry, model deployment, and code packaging. Discover the differences between the upstream project and Charmed MLFlow, Canonical's distribution. Learn about best use cases for Charmed MLFlow, when to integrate it with other MLOps platforms, and how to build an end-to-end MLOps solution. Delve into topics such as AI readiness, open source in machine learning, MLFlow's history and challenges, MLFlow projects, models, and experiments. Compare MLFlow with Kubernetes and explore its various applications. Conclude with a discussion on Clinical AI Roadshow and a Q&A session.