Kubeflow - Machine Learning over k8s
- MLOps Challenges
- Intro to Kubeflow
- Simplifying and extending Kubeflow with serverless ML functions
- Q&A with panelists
// Intro to Kubeflow As the involvement of Machine Learning components in software products is increasing rapidly, there is a growing need for tools to operate and orchestrate Machine Learning development and deployment. One of those tools is Kubeflow. Developed by Google for the Open Source community, this tool aims to organize the cycle of model development, testing, and deployment on the infrastructure of Kubernetes. In this talk, we will introduce the tool, its capabilities, and its integration in the infrastructure of organizations.
// Simplifying and extending Kubeflow with serverless ML functions
Serverless can simplify data science by automating the process of code to container and enables users to add instrumentation and auto-scaling with minimum overhead. However, serverless has many limitations involving performance, lack of concurrency, lack of GPU support, limited application patterns and limited debugging possibilities. Yaron Haviv will introduce Kubeflow, and how it works with Nuclio and MLRun, open source projects enabling serverless data-science and full ML lifecycle automation over Kubeflow.
Yaron will show real-world examples and a demo and explain how it can significantly accelerate projects time to market and save resources.
We will contact you as soon as possible.