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-mlflow-beta
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