AI news from Amazon Web Services – AWS

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Official Machine Learning Blog of Amazon Web Services
  1. Amazon Personalize is excited to announce automatic training for solutions. Solution training is fundamental to maintain the effectiveness of a model and make sure recommendations align with users’ evolving behaviors and preferences. As data patterns and trends change over time, retraining the solution with the latest relevant data enables the model to learn and adapt, […]
  2. We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues […]
  3. In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a slide deck into embeddings. We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) model to generate text responses to user questions based on […]
  4. This is a guest post co-written with the leadership team of Iambic Therapeutics. Iambic Therapeutics is a drug discovery startup with a mission to create innovative AI-driven technologies to bring better medicines to cancer patients, faster. Our advanced generative and predictive artificial intelligence (AI) tools enable us to search the vast space of possible drug […]
  5. As you navigate the complexities of cloud migration, the need for a structured, secure, and compliant environment is paramount. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources. This makes sure your cloud foundation is built according to AWS best practices from the start. With AWS Landing Zone, you eliminate the guesswork in security...
  6. You’ve likely experienced the challenge of taking notes during a meeting while trying to pay attention to the conversation. You’ve probably also experienced the need to quickly fact-check something that’s been said, or look up information to answer a question that’s just been asked in the call. Or maybe you have a team member that always joins meetings late, and expects you to send them a quick...
  7. 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 3 models are a collection of pre-trained and fine-tuned generative text models. In this post, we walk through how to discover and deploy Llama 3 models via SageMaker JumpStart. What is […]
  8. We are excited to announce that Slack, a Salesforce company, has collaborated with Amazon SageMaker JumpStart to power Slack AI’s initial search and summarization features and provide safeguards for Slack to use large language models (LLMs) more securely. Slack worked with SageMaker JumpStart to host industry-leading third-party LLMs so that data is not shared with the infrastructure owned by...
  9. In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking direct events like earnings reports or credit downgrades is straightforward—you can set up alerts to notify managers of news containing company names. However, detecting second and third-order impacts arising from...
  10. This post walks you through the Open Source Observability pattern for AWS Inferentia, which shows you how to monitor the performance of ML chips, used in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster, with data plane nodes based on Amazon Elastic Compute Cloud (Amazon EC2) instances of type Inf1 and Inf2.