Explore the key differences between Kubeflow and MLFlow in this 42-minute panel discussion featuring AI/ML experts from Canonical Ubuntu. Dive into production-grade and open-source MLOps, comparing these popular machine learning operations tools. Discover the pros and cons of each platform, learn about their similarities and differences, and gain insights on how to choose the right solution for your needs. Understand the core components of Kubeflow, including Notebooks for training, Pipeline for automation, and KServe for model serving, as well as MLFlow's four main concepts. Get acquainted with Canonical's Charmed Kubeflow distribution and its integrations with other tools. Explore topics such as community-driven ML tooling and Canonical's MLOps solution. Perfect for data scientists, ML engineers, and anyone interested in streamlining their machine learning workflows at scale.
Kubeflow vs MLFlow: Comparing Open-Source MLOps Platforms
Overview
Syllabus
Introduction
What is MLOps?
Open source MLOps
Kubeflow vs MLFlow: which one is better?
Kubeflow vs MLFlow: what is similar?
Kubeflow vs MLFlow: what is different?
Kubeflow vs MLFlow: how to choose?
Canonical’s MLOps solution
Taught by
Canonical Ubuntu
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