Andreea Munteanu
on 22 February 2021
Kubeflow has become quite popular in the MLOps community as the tool that enables data science teams to automate their workflows from data preprocessing to model deployment on Kubernetes.
However, with it’s made of many pieces, and while it keeps evolving, how can you effectively start using?
Learn Kubeflow from online courses
Started by Google, Kubeflow is a project which’s basics are presented on Coursera through a free training. During it, you will learn about
- TensorFlow Extended (or TFX), which is Google’s production machine learning platform
- How to automate your pipeline through continuous integration and continuous deployment
- How to manage ML metadata
- How to automate and reuse ML pipelines across multiple ML frameworks
Kubeflow training for the whole team
A possible fast-path, if you want to train all your team at once is Canonical’s offer of 4-day enterprise training. The training covers the following topics:
- Machine Learning & Deep Learning Architecture
- Introduction to Charmed Kubeflow, Canonical’s packaging of Kubeflow
- Kubeflow Pipelines and components
- MLOps and Advanced Topics
- Labs
Note: Canonical’s full offer of services can be found here
ML models in production
Building models is a totally different story than putting them in production. This is why we found this guide into how Tensorflow Extended (TFX) can help you move your models effectively, going through the whole process. The tutorial is not only a dry presentation of the steps that you need to follow, but a proper use case that you can have into production by the end of it.
If you would like to know more about Kubeflow, learn and understand more than the basic, you can take a look at these resources as well:
