AWS Developers | Build high performance & cost-effective ML apps using Amazon SageMaker- AWS Virtual Workshop @awsdevelopers | Uploaded 11 months ago | Updated 18 minutes ago
Managing the underlying infrastructure while building, training, and deploying machine learning (ML) models at scale can be technically intensive without the right tools and expertise. Amazon SageMaker is a fully managed ML service to build, train, and deploy ML models so you can focus on ML innovation instead of tedious infrastructure management. SageMaker offers you a choice of high-performance ML accelerators such as AWS Trainium and AWS Inferentia which are purpose-built for large-scale models such as LLMs and deliver 50% lower cost-to-train and 70% lower cost per inference. In this session, learn how you can build your own generative AI applications using Amazon SageMaker, AWS Trainium, and AWS Inferentia. In addition, we will also share how you can get started by using self-managed services such as AWS Deep Learning Container, AWS Deep learning AMIs, and ML frameworks and model libraries such as TensorFlow, PyTorch and Hugging Face.
***To learn more about the services featured in this talk, please visit: aws.amazon.com/machine-learning/infrastructure-innovation
To download the slides visit: pages.awscloud.com/rs/112-TZM-766/images/2023_VW-1012-MCL_Slide-Deck.pdf
#AWS
Managing the underlying infrastructure while building, training, and deploying machine learning (ML) models at scale can be technically intensive without the right tools and expertise. Amazon SageMaker is a fully managed ML service to build, train, and deploy ML models so you can focus on ML innovation instead of tedious infrastructure management. SageMaker offers you a choice of high-performance ML accelerators such as AWS Trainium and AWS Inferentia which are purpose-built for large-scale models such as LLMs and deliver 50% lower cost-to-train and 70% lower cost per inference. In this session, learn how you can build your own generative AI applications using Amazon SageMaker, AWS Trainium, and AWS Inferentia. In addition, we will also share how you can get started by using self-managed services such as AWS Deep Learning Container, AWS Deep learning AMIs, and ML frameworks and model libraries such as TensorFlow, PyTorch and Hugging Face.
***To learn more about the services featured in this talk, please visit: aws.amazon.com/machine-learning/infrastructure-innovation
To download the slides visit: pages.awscloud.com/rs/112-TZM-766/images/2023_VW-1012-MCL_Slide-Deck.pdf
#AWS
