Reducing Cost, Latency, and Manual Efforts in Hyperparameter Tuning at Redicell

Learn how to optimize hyperparameter tuning in machine learning models using Ray Tune in this conference talk. Discover techniques to reduce costs, latency, and manual efforts while building and experimenting with ML/DL models. Explore the benefits of Ray Tune's out-of-the-box features for efficient compute resource management and its scheduling algorithms for pruning bad trials. Gain insights into integrating Ray Tune with tools like MLflow and Weights & Biases for streamlined experiment tracking and logging. Follow along with a demo and learn how to implement these strategies to enhance your model training process.

Introduction
What is hyperparameter tuning
Asynchronous hyperband scheduler
Demo
Questions