Simplify Scaling Machine Learning and Python Workloads

Wednesday, October 12, 6:00PM UTC

Watch this on-demand webinar to learn how to simply scale any AI or Python workload using Ray, the fastest growing unified compute framework for scaling machine learning workloads.

Learn how users are using Ray to build, manage and deploy scalable ML workloads.

See more highlights below:

  • Instant workload scaling from your laptop to the cloud with minimal code changes and a single script for data preparation, training, tuning and deploying ML workloads.

  • Batch training and tuning with 9-22x performance boosts when compared to alternatives.

  • Unstructured data processing simplified from ingestion for complex datasets including images, transcripts, text, documents, logs, audio and video at scale.

  • Large-scale inference and simulation through embarrassingly parallel experiments such as drug discovery, simulation-based experiments and backtesting at scale.

Watch now to learn how you can accelerate your time-to-value with Ray!


Phi Nguyen

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

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.