Webinar
Simplify and scale your XGBoost model using Ray on Anyscale
Wednesday, March 9, 5:00PM UTCIn this webinar, we will cover how Ray, a universal distributed computing framework running on Anyscale, simplifies the end-to-end machine learning lifecycle and provides serverless compute without limits. We will go through an example from beginning to end using XGBoost.
See first hand how to:
Load data with Ray Datasets
Train an XGBoost model on Ray
Perform hyperparameter tuning with Ray Tune
Scale from your laptop to Anyscale with zero code changes
Experiment tracking with Weight and Biases
Resources
Speakers
Phi Nguyen
GTM Tech Lead, Anyscale
Phi has been working with Fortune 500 customers in Retail, CPG, HCLS, Financial services and startups to accelerate their machine learning practices. This includes a wide range of engagements such as helping teams organize and build a center of excellence for ML, MLOps processes and automation, ML use cases development and feasibility to providing cloud best practices combining Ray and public cloud such as AWS and GCP or open source projects running on Kubernetes.
Antoni Baum
Software engineer, Anyscale
Antoni is a Software Engineer at Anyscale, working on Ray Tune and other ML libraries, and a Computer Science & Econometrics MSc student. In his spare time, he contributes to various open source projects, trying to make machine learning more accessible and approachable.