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Kubeflow vs MLFlow

Learn the main differences between the MLOps tools of choice: Kubeflow and MLFlow Started by Google a couple of years ago, Kubeflow is an end-to-end MLOps platform for AI at scale. Canonical has its own distribution, Charmed Kubeflow, which addresses the entire machine-learning lifecycle. Charmed Kubeflow is a suite of tools, such as Notebooks for training, Pipeline for automation, Katib for hyperparameter tuning or KServe for model serving and more. Charmed Kubeflow benefits from a wide range of integrations with other tools such as MLFlow, Spark, Grafana or Prometheus. MLFLow on the other hand celebrated 10 million downloads last year. It’s a very popular solution when it comes to machine learning. Although it started initially with a core function, the tool has nowadays four conceptions that include model registry or experiment tracking. So, which one should you choose for Machine Learning Operations? Join us for a Kubeflow vs MLFLow panel discussion with Maciej Mazur, AI/ML Principal Engineer at Canonical, and Kimonas Sotirchos - Kubeflow Community Working Group Lead and Engineering Manager at Canonical. The discussion will cover: Production-grade MLOps Open-source MLOps Community-driven ML tooling Kubeflow vs MLFlow; Pros and Cons Further reading: Whitepaper: A guide to MLOps: https://ubuntu.com/engage/mlops-guide Charmed Kubeflow: https://charmed-kubeflow.io/ Try out Charmed MLFlow Beta: https://ubuntu.com/blog/charmed-mlflo... Key moments: 0:00 Introduction 4:52 What is MLOps? 8:10 Open source MLOps 10:50 Kubeflow vs MLFlow: which one is better? 26:10 Kubeflow vs MLFlow: what is similar? 28:23 Kubeflow vs MLFlow: what is different? 30:55 Kubeflow vs MLFlow: how to choose? 34:18 Canonical’s MLOps solution #mlops #machinelearning #kubernetes