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Kubeflow news

Your weekly update of curated kubeflow news from across the web.

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Kubeflow operations guide

17 March 2021

Operating MLOps stacks alike Kubeflow in an increasingly multi-cloud world will be a key topic as this market and Kubernetes adoption grow.

Kubeflow operations webinar

To discuss this topic, Canonical is holding a live webinar next week, on 23rd of March, 5PM UTC. Besides the key points listed below, the webinar will also have a live demo:

  • How challenges of operating Kubeflow and how cloud-native operations are evolving
  • What are Kubernetes operators, Charms and OLMs and why they are a game-changer
  • How model-driven operators can automate day-0 to day-2 operations of Kubeflow even for complex setups
  • What is Charmed Kubeflow and how it can be deployed with your Kubernetes of choice

Did you prepare your questions?

Read the book!

For a deeper dive into this topic, and an alternative approach to Canonical’s, check out last year’s O’Reilly book by Josh Patterson, Michael Katzenellenbogen and Austin Harris.

You can read more before you buy it!

Latest community videos

8 March 2021

MLOps community jewels

The MLOps community continues to grow and gift us with great content and discussions around the topic!

Here are a couple of interesting discussions – a long one (1h) about Kubeflow, feature stores, and other platforms in the MLOps space, and a short one (3 min) on how to manage dependencies:

Sneak peek on Kubeflow v1.3

The Arrikto team has been leading the Kubeflow v1.3 release and making fantastic contributions, including some additions to the Kubeflow dashboard, like Volumes and Models tabs.

Kimonas Sotirchos, full-stack engineer @Arrikto, gives us a quick tour on the new UI for managing data and Persistent Volume Claims (PVCs):

Still figuring out what is Kubeflow?

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
  • 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.

Source: Tensorflow

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:

SageMaker and Kubeflow: end-to-end ML workflows

11 February 2021

In June 2020, AWS introduced SageMaker components for Kubeflow. 6 months later, Antje Barth, Sr. developer advocate @AWS, presents how to build end-to-end ML workflows with Kubeflow Pipelines and how to leverage the benefits of Kubeflow Pipelines and SageMaker altogether.

AWS re:invents end-to-end ML workflows

Watch the video below:

If you are more curious, there is an entire stack of articles around SageMaker& Kubeflow

Kubeflow impact from health to telco

8 February 2021

From Gitlab to Kubeflow in Healthcare ML

Lifen, the french platform for healthcare products, recently switched from Gitlab’s jobs to Kubeflow Pipelines for continuous learning capabilities and showcases the transition and its benefits.

Check out the blog post

Kubeflow for AI in the Telco industry

Maciej Mazur, Product Manager @Canonical for Telco and AI/ML shares his insights on how to approach data science in the Telco industry, including how Kubeflow can be a key asset for innovation.

Read more