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• Kubeflow vs MLFlow

Kubeflow vs MLFlow

• Learn the main differences between the MLOps tools of choice: Kubeflow and MLFlow
• Whitepaper: A guide to MLOps: https://ubuntu.com/engage/mlops-guide
• Charmed Kubeflow: https://charmed-kubeflow.io/

Abstract

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