Efficient AutoML with Ludwig, Ray, and Nodeless Kubernetes
Offered By: CNCF [Cloud Native Computing Foundation] via YouTube
Course Description
Overview
Explore how open-source platforms Ludwig and Ray democratize Deep Learning by simplifying the process of training, scaling, deploying, and serving models. Dive into Ludwig's recent AutoML extensions for tabular and text classification datasets, leveraging Ray Tune for efficient hyperparameter search. Learn about the heuristics employed by Ludwig AutoML to produce effective models for validation datasets. Discover the cost-saving and operational benefits of running Ludwig AutoML on cloud Kubernetes clusters with Nodeless K8s, which dynamically allocates and removes GPU resources as needed, compared to running directly on EC2 instances.
Syllabus
Efficient AutoML with Ludwig, Ray, and Nodeless Kubernetes - Anne Marie Holler + Travis Addair
Taught by
CNCF [Cloud Native Computing Foundation]
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent