This is a follow-up analysis of our prior...

We are excited to announce two new capabilities in Amazon SageMaker Studio that will accelerate iterative development for machine learning (ML) practitioners: Local Mode...

IntroductionString theory captured the hearts and minds of many physicists decades ago because of a beautiful simplicity. Zoom in far enough on a patch...

COMMENTARYOWASP recently released its top 10 list for large language model (LLM) applications, in an effort to educate the industry on potential security threats to be...

Generative artificial intelligence (AI) has gained significant momentum with organizations actively exploring its potential applications. As successful proof-of-concepts transition into production, organizations are increasingly...

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training...

Amazon Personalize is excited to announce automatic training for solutions. Solution training is fundamental to maintain the effectiveness of a model and make sure...

We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is...

In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a...

This is a guest post co-written with the leadership team of Iambic Therapeutics. Iambic Therapeutics is a...

Migrating to the cloud is an essential step for modern organizations aiming to capitalize on the flexibility and scale of cloud resources. Tools like...

Today, we are excited to announce that Meta Llama 3 foundation models are available through Amazon SageMaker JumpStart to deploy and run inference. The Llama...