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AI on Kubernetes series

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AI on Kubernetes overview

Summary

Does it makes sense to run AI workloads on Kubernetes? Pros, cons and how to start

Presenter

Václav Pavlín, [email protected]

Slides


Upcoming

Kubeflow & Open Data Hub components overview

Summary

Let's go over all the available components of Kubeflow and Open Data Hub projects and explore what technologies are available and what problems they can help you solve.

Presenter

Slides

Extending Kubeflow

Summary

The journey of Open Data Hub and how we are turning KF into downstream enterprise grade distribution

Presenter

Slides

Deploying Jupyter on Kubernetes

Summary

JupyterHub vs. notebook-controller, multitenancy, extendability, scaling

Presenter

Slides

Using Jupyter notebooks on Kubernetes

Summary

An expert will walk through how to to use Jupyter Notebooks (not only on Kubernetes) for successful data analysis

Presenter

Slides

Workflows and Pipelines

Summary

Workflows and pipelines are core components of data analysis. What are our options on Kubernetes? How to create a simple pipeline? What are the caveats?

Presenter

Slides

Experiment tracking

Summary

What tools do we have on Kubernetes to strategically track our experiments and build automated pipelines for dev to prod model deployment?

Presenter

Slides

Distributed training of AI/ML models on Kubernetes

Summary

Open Source offers a great deal of AI/ML frameworks for model creation. Let’s see which of these have good support on Kubernetes and how to use them.

Presenter

Slides

AI on Edge

Summary

How can we move beyond a massive data center clusters to the edge for our AI workloads?

Presenter

Slides