Explore the latest updates from the AutoML and Training Working Groups in this informative conference talk. Gain insights into Katib's 2024 roadmap, including V1 APIs graduation, advanced parameter distribution support, and large language model tuning capabilities. Discover the future of Training Operator, featuring LLM API support via Python SDK, advanced suspend semantics, and indexed job support. Learn about the Kubernetes-native AutoML features and scalable distributed training solutions offered by Kubeflow, empowering machine learning practitioners and researchers in cloud-native environments.
AutoML and Training Working Group Updates for Kubeflow
CNCF [Cloud Native Computing Foundation] via YouTube
Overview
Syllabus
AutoML & Training Working Group Updates - Andrey Velichkevich, Yuki Iwai, Johnu George, Amber Graner
Taught by
CNCF [Cloud Native Computing Foundation]
Related Courses
-
AutoML and Training Working Group Updates - Kubeflow Developments
-
Building AutoML Pipelines with Argo Workflows and Katib
-
Efficient Integration of Kubeflow Tools - ISS Data Case Study on Anomaly Detection
-
A Scalable Platform for Training and Inference Using Kubeflow at CERN
-
Build NLP Model with Azure ML AutoML NLP Capability
-
Efficient Deep Learning Training with Ludwig AutoML, Ray, and Nodeless Kubernetes
