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