Arindam Paul

Struggling with large language model (LLM) deployment complexity? Join this session to learn how vLLM optimized AWS Deep Learning Containers revolutionize the way we deploy large language models like DeepSeek-R1-Distill-Qwen-32B on Amazon EKS.

Join this chalk talk for a deep dive into AWS Deep Learning Containers, and explore how it can be used to fine-tune Llama 3.2. Discover how to use Deep Learning Containers to rapidly scale your ML infrastructure, reduce the total cost of infrastructure ownership, and benefit from performance, security, and stability.

Join this session for a deep dive into the infrastructure used to train large models at Pinterest. It covers training hardware, compute orchestration, and ML application development SDKs as well as how infrastructure choices speed up development and improve model training efficiency.

AWS is accelerating contributions to open-source technologies like Kubernetes and Kubeflow to streamline machine learning training and inference tasks. In this chalk talk, learn how ML developers and data scientists can use AWS open-source contributions, Amazon SageMaker Operators for Kubernetes, and SageMaker components for Kubeflow to build secure, scalable, and highly available machine...