Today, we are excited to announce that Mercury and Mercury Coder foundation models (FMs) from Inception Labs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Mercury FMs to build, experiment,...
Healthcare discovery on ecommerce domains presents unique challenges that traditional product search wasn’t designed to handle. Unlike searching for books or electronics, healthcare queries involve complex relationships between symptoms, conditions, treatments, and services, requiring sophisticated understanding of medical terminology...
As data becomes more abundant and information systems grow in complexity, stakeholders need solutions that reveal quality insights. Applying emerging technologies to the geospatial domain offers a unique opportunity to create transformative user experiences and intuitive workstreams for users...
Amazon SageMaker HyperPod is a purpose-built infrastructure for optimizing foundation model (FM) training and inference at scale. SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training FMs, reducing training time...
Every day, organizations process millions of documents, including invoices, contracts, insurance claims, medical records, and financial statements. Despite the critical role these documents play, an estimated 80–90% of the data they contain is unstructured and largely untapped, hiding valuable...
Most organizations evaluating foundation models limit their analysis to three primary dimensions: accuracy, latency, and cost. While these metrics provide a useful starting point, they represent an oversimplification of the complex interplay of factors that determine real-world model performance.
Foundation...
