Getting started with Kubeflow
Who is this presentation for?
- Data scientists and AI infrastructure engineers
Kubeflow consists of dozens of components used to train and serve machine learning models, but the first task is to understand the Kubeflow architecture and how it fits into the larger Kubernetes ecosystem. Skyler Thomas details the performance, availability, and security impacts of various Kubeflow deployment options and the decisions you make in how you use Kubeflow.
Once you understand the basics, it’s time for you to go hands on and actually deploy a subset of the Kubeflow components into a running Kubernetes environment using Jupyter Hub and Jupyter notebooks to import your training data and set up prerequisites for creating training environments using TensorFlow (and PyTorch, time permitting). You’ll begin to submit training jobs and generate and serve models via TensorFlow Serving (and Seldon Core, time permitting).
- A working knowledge of GitHub (The ability to clone a GitHub repo is required.)
- General knowledge of Kubernetes and various machine learning frameworks like TensorFlow and PyTorch (useful but not required)
Materials or downloads needed in advance
- A Wi-Fi enabled laptop with connectivity to GitHub and a >v1.11 Kubernetes environment (This environment can be a Minikube installation, but a Kubernetes cluster with several nodes available is recommended.)
What you'll learn
- Understand the architecture of Kubeflow
- Learn to install Kubeflow into Kubernetes, use Jupyter Notebooks, start a TensorFlow training job, and serve your models to handle requests
Skyler Thomas is the chief architect for Kubernetes and a principal engineer at MapR, where he Kubernetes-based infrastructure to deliver machine learning and big data applications at scale. Previously, Skyler was a lead architect for WebSphere at IBM, where he worked with hundreds of customers to deliver extreme-scaled applications in various industries, including healthcare and financial services.
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