Frank McQuillan

With Amazon EKS support in Amazon SageMaker HyperPod, users can deploy foundation models for inference on a performant and resilient cluster. They can leverage state-of-the-art model servers in SageMaker and an ecosystem of Kubernetes tools.

Amazon SageMaker Feature Store is a fully managed, purpose-built service to store, share, and manage features for ML models. In this chalk talk, walk through multiple real-world use cases and see how to use SageMaker Feature Store to create features, ingest data, and build transformation pipelines.

In this session we will present an efficient way to train many deep learning model configurations at the same time with Greenplum, a free and open source massively parallel database based on PostgreSQL. The implementation involves distributing data to the workers that have GPUs available and hopping model state between those workers, without sacrificing reproducibility or accuracy.

Advanced analytics and machine learning are rapidly growing in importance in enterprise computing. Key enterprise data typically resides in relational form, and it is inefficient to copy data between systems to perform analytical operations.

In this session we will discuss the use of massively parallel databases for deep learning, drawing on experience from running deep learning frameworks like Keras and TensorFlow with GPU acceleration using free and open source software like Greenplum Database and the Apache MADlib machine learning library. Topics will include architecture, common usage patterns, scalability results and bright…