Ray has quickly become the distributed framework of choice for training, tuning and serving Generative AI and Large Language Models (LLMs). The foundation of any LLM/Generative AI effort is the ability to have access to high quality data. Cloudera provides an open data lakehouse that is secure by design and flexible to fit scenarios ranging from stream processing, data warehousing, data engineering, and AI and advanced ML.
In this session, we will show you how Cloudera Machine Learning makes it easy to spin up Ray clusters on Kubernetes with a couple lines of code. This enables data scientists to leverage the power of Ray on top of enterprise data. With this data loaded up into a Ray dataset, we will finish showing how the data can be transformed into features and used to train and deploy an ML model.
Chris is a Principal Product Manager for Cloudera Machine Learning. He is focused on helping customers leverage machine learning and advanced analytics to realize value and gain business insights from their data. He has Energy industry experience and served as an Industry CTO for Microsoft before joining Cloudera. Chris is a graduate of Texas A&M University.
Come connect with the global community of thinkers and disruptors who are building and deploying the next generation of AI and ML applications.