Adam Breindel
Adam Breindel is a member of the Anyscale training team and he consults and teaches on large-scale data engineering and AI/machine learning. He has served as technical reviewer for numerous O'Reilly titles covering Ray, Apache Spark, and other topics. Adam's 20 years of engineering experience include numerous startups and large enterprises with projects ranging from AI/ML systems and cluster management to web, mobile, and IoT apps. He holds a BA (Mathematics) from University of Chicago and a MA (Classics) from Brown University. Adam's interests include hiking, literature, and complex adaptive systems.
Sessions
Machine learning (ML) pipelines involve a variety of computationally intensive stages. As state-of-the-art models and systems demand more compute, there is an urgent need for adaptable tools to scale ML workloads. This idea drove the creation of Ray—an open source, distributed ML compute framework that not only powers systems like ChatGPT but also pushes theoretical computing benchmarks. Ray AIR is especially useful for parallelizing ML workloads such as pre-processing images, model training and finetuning, and batch inference. In this tutorial, participants will learn about AIR’s composable APIs through hands-on coding exercises.