机器学习平台架构系列-1-klubeflow

开源系列

cube开源一站式云原生机器学习平台：https://blog.csdn.net/luanpeng825485697/article/details/123619334
github:https://github.com/tencentmusic/cube-studio

kubeflow参考

官网：https://www.kubeflow.org/docs/started/
参考：https://www.jianshu.com/p/192f22a0b857
AirFlow/NiFi/MLFlow/KubeFlow进展：https://blog.csdn.net/chenhuipin1173/article/details/100913909
最好的任务编排工具：Airflow vs Luigi vs Argo vs MLFlow

总结

一句话总结就是：kubeflow是一个为 Kubernetes 构建的可组合，便携式，可扩展的机器学习技术栈。
支持的训练架构-https://www.kubeflow.org/docs/components/training/

英文对比：
https://aicurious.io/posts/airflow-mlflow-or-kubeflow-for-mlops/
https://devsamurai.vn/blog/ml-platform-kuberflow-mlflow-argo-airflow/

通用型选airflow
机器学习偏向大规模选kubeflow
机器学习偏向小规模选mlflow

5. How to choose between Airflow+Mlflow, and Kubeflow?

To sum up, I have some recommendations from my personal perspective:

    If your system needs to deal with multiple types of workflow, not just machine learning, Airflow may support you better. It is a mature workflow orchestration frameworks with support for a lot of operators besides machine learning.
    If you want a system with predesigned patterns for machine learning, and run at large scale on Kubenetes clusters, you may want to consider Kubeflow. Many ML specific components in Kubeflow can save your time implementing from scratch in Airflow.
    If you want to deploy MLOps in a small scale system (for example, a workstation, or a laptop), picking Airflow+MLflow stack can eliminate the need of setting up and running a Kubenetes system, and save more resources for the main tasks.

This blog post has briefly shown the differences between three popular MLOps frameworks (Airflow, MLflow and Kubeflow). Hope that it helps you in making decision between 2 stacks (Airflow + MLflow and Kubeflow). If you want to talk more about these frameworks or recommend others, please comment beflow. Thank you very much!
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