Speaker
Description
One of the main challenges for Kubernetes adoption in businesses is the lack of in-house skills to make the most out of a Kubernetes-based stack which is especially true for organizations implementing Machine Learning workloads developed by ML Engineers or Data Scientists whose skill set usually does not include infrastructure tooling from the cloud native ecosystem.
In this talk, you'll learn about key Kubernetes constructs, why and how to use them effectively to meet core requirements for ML workloads as we introduce Kubeflow which is an open-source project allowing users to leverage the power of Kubernetes to run the training and serving of their ML models. We will focus on Charmed Kubeflow which is a Canonical distribution of Kubeflow to show how you can leverage the power of Kubeflow to deploy and serve large machine-learning models with ease.
What audience can learn
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Learn how to properly deploy LLMs and modern ML pipelines with ease on Kubernetes.
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This is a great session for MLOps, DevOps engineers to understand tooling that can be leveraged for efficient ML provisioning with a focus on scale and efficient and cost friendly deployments of ML pipelines
Biography
I'm Shivay Lamba, a software engineer specializing in Web Development, Machine Learning, and DevOps. I am also a Developer Relations Consultant helping various startups improve their developer experience. I am also an open-source contributor, maintainer, and mentor.
Things to know or prepare for this session
Basics of Machine learning, and Kubernetes
Summary
In this talk, we introduce Kubeflow an open-source project allowing users to leverage the power of Kubernetes to run the training and serving of their ML models. We will focus on Charmed Kubeflow which is a Canonical distribution of Kubeflow to show how you can leverage the power of Kubeflow
| Difficulty level | Intermediate |
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