From Kubernetes to Kubeflow
About
Kubeflow is a specialized ML platform that is built for Kubernetes and runs in Kubernetes clusters as a collection of Pods and Operators. Kubeflow harnesses the power of Kubernetes to orchestrate containerized environments allowing enterprises to optimize the path from development to production. Kubeflow provides container images to run ML workloads and IDEs, such as JupyterLab Notebooks. Kubeflow is a Data Scientist obsessed platform that leverages the power of Kubernetes to really improve the Model Development Lifecycle by abstracting away the K8s complexity so Data Scientists can focus on data science. This workshop is for those who are interested in exploring the necessary Kubernetes components to support Kubeflow and how those components are extracted away for your benefit. The agenda is as follows:
- Kubeflow Notebooks
- Kubernetes and Kubeflow
- Using Notebook Servers in Academy
Requirements
We assume that you have basic familiarity with cloud computing environments like AWS, GCP or Azure as well as a basic understanding of cloud-native architectures and Kubernetes concepts like pods, controllers, nodes, container images, volumes, etc. Additionally we assume that you have familiarity with ML concepts like algorithms, model training, and parameter tuning.