How to bring machine learning workloads on to Kubernetes using Kubeflow
Presentation will have a walkthrough of how Kubeflow can help you achieve deployment of machine learning workloads in an orchestrated way on Kubernetes.
Objective of the session:
- To get people aware about the features of Kubernetes and how those can be used for a rapidly growing industry like Machine Learning. How to get ML workloads deployments on Kubernetes in a simple, portable and scalable manner.
Who can attend this session?
- Anyone who is willing to learn more about power of Containerization and Kubernetes.
What all will be covered in the session?
- Session will have a walkthrough of Kubeflow and a quick demo to showcase the same.
Benefits/Take away for the attendees:
- Getting familiar with kubeflow
- Kubeflow pipelines
- End to end deployment of ML solution on Kubeflow pipeline
Pre-requisites to attend the session:
- Attendees should have basic understanding of Machine Learning and Kubernetes to gain maximum out of the session. Familiarity with features of Kubernetes will make it easy to run through the session
Experienced Cloud Architect
Experienced Cloud Architect with a demonstrated history of working in the information technology and services industry. Skilled in Devops, AWS/Azure Cloud and VMware Infrastructure. Strong information technology professional with a Bachelor’s Degree focused in Electronics and Communications Engineering from University of Rajasthan.