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1
Introduction
2
KDP Overview
3
KDP Architecture
4
Why Algo Workflows
5
Memorization Cache
6
Template Spec
7
Example Workflow
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Entry Point
9
MultiObjective Optimization
10
Implementation
11
Demo
12
Community
13
QA
Description:
Explore the integration of Automated Machine Learning (AutoML) with cloud-native technologies in this conference talk. Learn how to manage thousands of complex hyperparameter tuning experiments using Argo and Katib for optimal performance. Discover best practices, including Argo caching and synchronization, for efficiently developing and deploying AutoML algorithms in production environments. Gain insights into Kubernetes-native workflow orchestration and hyperparameter tuning at scale through practical demonstrations and examples. Understand the architecture of KDP, the benefits of algorithmic workflows, and the implementation of multi-objective optimization. Conclude with a live demo and community discussion, equipping you with valuable knowledge to advance your MLOps capabilities.

Managing Thousands of Automatic Machine Learning Experiments with Argo and Katib

CNCF [Cloud Native Computing Foundation]
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