Kubeflow vs MLFlow

Artificial Intilligence and Machine Learning are hot topics these days, with more enterprises announcing huge investments in powerful computing hardware and new LLM models all over the news. It translates into a clear need to also optimise and search for machine learning tooling, that enables the desired return on investment for all these projects. Kubeflow vs MLFlow Started by Google a couple of years ago, Kubeflow is by design an end-to-end MLOps platform for AI at scale. Canonical has its own distribution, Charmed Kubeflow addresses the entire machine-learning lifecycle. It is, in fact, 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 last year 10 million downloads, being a very popular solution when it comes to machine learning. Started initially with a core function, the tool has nowadays four conceptions that include model registry or experiment tracking. So, the main question arises: which one should you choose for Machine Learning Operations? Kubeflow vs MLFLow is a panel discussion with Maciej Mazur - AI/ML Principal Engineer at Canonical, Kimonas Sotirchos - Kubeflow Community Working Group Lead and Engineering Manager at Canonical about: Production-grade MLOps Open-source MLOps Community-driven ML tooling Kubeflow vs MLFlow; Pros and Cons