Ray 2.2 boosts machine learning observability and scalability performance

Check out all the on-demand sessions from the Intelligent Security Summit here. Ray, the popular open-source machine learning (ML) framework, has released its 2.2 version with improved performance and observability capabilities, as well as features that can help to enable reproducibility. The Ray technology is widely used by organizations to scale ML models across clusters of hardware, for both training and inference. Among Ray's many users is generative AI pioneer OpenAI, which uses Ray to scale and enable a variety of workloads, including supporting ChatGPT. The lead commercial sponsor behind the Ray open-source technology is San Francisco-based Anyscale, which has raised $259 million in funding to date.