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Kubeflow news

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

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



Latest Kubeflow posts

26 January 2021

Scaling Keras on Kubernetes with Kubeflow

In this week’s blog post, Kirill Goltsman has deconstructed how to use Kubeflow, TFOperator, MPI Operator to train and deploy Keras models at scale.

Check out his blog post!

How Kubeflow & MLOps can help security

David Aronchick, co-founder of Kubeflow and head of OSS ML Strategy at Microsoft explains how you can use Kubeflow and MLOps to secure your AI/ML workloads.

Watch the video below:


Canonical Kubeflow Operators

5 November 2020

Canonical, the publisher of Ubuntu, announces Kubeflow operators and packages.

Within the last week, Canonical announced two new technologies that aim at improving the Kubeflow experience:

  1. Charmed Kubeflow – A set of Kubeflow charm operators, that leverage Juju OLM technology for lifecycle management of the applications inside Kubeflow. Read the announcement.
  2. Lightweight Kubeflow bundles – two new packages of pre-selected applications from the Kubeflow bundle to fit desktop (Kubeflow lite) and edge scenarios (Kubeflow edge). Read blog post.

Kubeflow 1.1 is out!

24 August 2020

New release, increased capabilities.

After 6 months since the release of 1.0, Kubeflow releases a new version with increased capabilities.

This new version has focused on improving ML Workflow Productivity, Isolation and Security, and GitOps. Here is a list of the enhanced features:

  1. Fairing and Kale (Kubeflow Automated pipeLines Engine) for end-to-end workflows.
  2. Katib hyperparameter tuning:
    1. New frameworks & algorithms (goptuna with CMA-ES, DARTS, chocolate, hyperopt, skopt).
    2. Flexible config & tuning options (new python SDK, experiments with undefined goal, new UI, new resume policy).
  3. Install and operations to support GitOps, using blueprints and kpt primitives.
  4. Isolation and security: multi-user Kubeflow pipelines, CVE scanning, and support for Google’s Private GKE and Anthos.
  5. MXNet and XGBoost distributed training operators, simplify training on multiple nodes, and speeds model creation.
  6. Seldon Core 1.1

What’s next:

  • Read the official release blog post here.
  • Suggest new features for version 1.2 on this thread!


Kubeflow latest events

17 July 2020

Kubeflow dojo by IBM

IBM organizes a 2-day Kubeflow dojo to get you up to speed on Kubeflow. If you did not have a chance to attend, check out the videos and slides here:

Kubeflow Dojo by IBM

Pipelines webinar by Canonical

Canonical hosts one webinar alongside blog series to demystify Kubeflow pipelines and help you convert your notebooks into pipelines:

Kubeflow pipelines webinar + demo + blog series.


Weekend watch list

25 June 2020

Kubeflow 101 – Hyperparameter tuning with Katib

The Kubeflow 101 series of short videos is a great way to quickly get up to speed on Kubeflow concepts. This week, Stephanie Wong guides us through Hyperparameters and how you can use Katib to achieve the

Kubeflow with Amazon Sagemaker

Shashank Prasanna, Senior Developer Advocate at AWS walks viewers on how to get the best out of Kubeflow and Amazon Sagemaker, in the same stack.


Security breach

12 June 2020

Microsoft exposes attacks to Kubeflow deployments

Microsoft publishes report detailing series of attacks against clusters running Kubeflow with the purpose of mining cryptocurrencies. To ensure that you are on the safe side, follow the steps below:

1. When deploying Kubeflow, make sure that its dashboard isn’t exposed to the internet: check the type of the Istio ingress service by the following command and make sure that it is not a load balancer with a public IP:

kubectl get service istio-ingressgateway -n istio-system

2. Verify that the malicious container is not deployed in the cluster, through the following command:

kubectl get pods –all-namespaces -o jsonpath=”{.items[*].spec.containers[*].image}”  | grep -i ddsfdfsaadfs

Find out more about this news – ZDNet, threatpost


Amazon + Kubeflow

4 June 2020

SageMaker Embraces Kubeflow Pipelines

Amazon announced this week the possibility to configure Kubeflow Pipelines to run ML jobs with Amazon SageMaker.

This is yet another validation of Kubeflow as a widespread solution and reinforces the idea of ML workflows on Kubernetes. Read the post here.


Kubeflow 101

27 May 2020

Kubeflow in 3 minutes

Google launches Kubeflow 101, a series of short videos to demystify Kubeflow. Intro to Kubeflow. Watch an overview of Kubeflow in the first episode:

Intro to Kubeflow pipelines

In the second episode of Kubeflow 101, watch an introduction to Kubeflow pipelines, and the presentation of a new hosted version of Kubeflow



Weekend watch list

6 March 2020

CNCF introduction to Kubeflow

Arun Gupta, Senior Engineering Manager at Apple walks viewers through the benefits of Kubeflow and how to get started.

Securing Kubeflow

Talk by Barton Rhodes, Senior Machine Learning Engineer at DaVita Inc. This talk is motivated by Ian Coldwater during the Kubernetes Podcast ‘Attacking and Defending Kubernetes:

Containers are only as secure as their runtimes, and their orchestration frameworks, and their kernels, and their operating systems, and everything else”

Building and monitoring Kubeflow

Kirk Kaiser, APM at Datadog, talks about how to build and monitor Kubeflow machine learning Services with examples for an art project.