This post was written with Audra Devoto, Owen Janson, and Christopher Brown of Metagenomi, and Adam Perry of Tennex. A promising strategy to augment the extensive natural diversity of high value enzymes is to use generative AI, specifically protein language models (pLMs), trained on known enzymes to create orders ofContinue Reading
Generative AI is rapidly reshaping the music industry, empowering creators—regardless of skill—to create studio-quality tracks with foundation models (FMs) that personalize compositions in real time. As demand for unique, instantly generated content grows and creators seek smarter, faster tools, Splash Music collaborated with AWS to develop and scale music generationContinue Reading
Training state-of-the-art large language models (LLMs) demands massive, distributed compute infrastructure. Meta’s Llama 3, for instance, ran on 16,000 NVIDIA H100 GPUs for over 30.84 million GPU hours. Amazon Elastic Kubernetes Service (Amazon EKS) is a managed service that simplifies the deployment, management, and scaling of Kubernetes clusters that canContinue Reading
Organizations building custom machine learning (ML) models often have specialized requirements that standard platforms can’t accommodate. For example, healthcare companies need specific environments to protect patient data while meeting HIPAA compliance, financial institutions require specific hardware configurations to optimize proprietary trading algorithms, and research teams need flexibility to experiment withContinue Reading
Private workforces for Amazon SageMaker Ground Truth and Amazon Augmented AI (Amazon A2I) help organizations build proprietary, high-quality datasets while keeping high standards of security and privacy. The AWS Management Console provides a fast and intuitive way to create a private workforce, but many organizations need to automate their infrastructureContinue Reading
As generative AI continues to transform how enterprises operate—and develop net new innovations—the infrastructure demands for training and deploying AI models have grown exponentially. Traditional infrastructure approaches are struggling to keep pace with today’s computational requirements, network demands, and resilience needs of modern AI workloads. At AWS, we’re also seeingContinue Reading
As organizations increasingly adopt foundation models (FMs) for their artificial intelligence and machine learning (AI/ML) workloads, managing large-scale inference operations efficiently becomes crucial. Amazon Bedrock supports two general types of large-scale inference patterns: real-time inference and batch inference for use cases that involve processing massive datasets where immediate results aren’tContinue Reading
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 and customer intent. This challenge became particularly relevant for Amazon asContinue Reading
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 insights that could transform business outcomes. Despite advances in technology, manyContinue Reading
The MERN (MongoDB, Express, React, Node.js) stack is a popular JavaScript web development framework. The combination of technologies is well-suited for building scalable, modern web applications, especially those requiring real-time updates and dynamic user interfaces. Amazon Q Developer is a generative AI-powered assistant that improves developer efficiency across the differentContinue Reading
