Category: *Post Types

Today, Amazon SageMaker is excited to announce updates to the inference optimization toolkit, providing new functionality and enhancements to help you optimize generative AI models even faster.In this post, we discuss these new features of the toolkit in more detail.

In this post, we explore how Syngenta collaborated with AWS to develop Cropwise AI, a generative AI assistant powered by Amazon Bedrock Agents that helps sales representatives make better seed product recommendations to farmers across North America. The solution transforms the seed selection process by simplifying complex data into natural conversations, providing quick access to detailed seed product information, and enabling personalized recommendations at scale through a mobile app interface.

At re:Invent 2024, we are excited to announce new capabilities to speed up your AI inference workloads with NVIDIA accelerated computing and software offerings on Amazon SageMaker. In this post, we will explore how you can use these new capabilities to enhance your AI inference on Amazon SageMaker. We’ll walk through the process of deploying NVIDIA NIM microservices from AWS Marketplace for SageMaker Inference. We’ll then dive into NVIDIA’s model offerings on SageMaker JumpStart, showcasing how to access and deploy the Nemotron-4 model directly in the JumpStart interface. This will include step-by-step instructions on how to find the Nemotron-4 model in the JumpStart catalog, select it for your use case, and deploy it with a few clicks.

Today at AWS re:Invent 2024, we are excited to announce the new Container Caching capability in Amazon SageMaker, which significantly reduces the time required to scale generative AI  models for inference. This innovation allows you to scale your models faster, observing up to 56% reduction in latency when scaling a new model copy and up to 30% when adding a model copy on a new instance. In this post, we explore the new Container Caching feature for SageMaker inference, addressing the challenges of deploying and scaling large language models (LLMs).

Today at AWS re:Invent 2024, we are excited to announce a new capability in Amazon SageMaker Inference that significantly reduces the time required to deploy and scale LLMs for inference using LMI: Fast Model Loader. In this post, we delve into the technical details of Fast Model Loader, explore its integration with existing SageMaker workflows, discuss how you can get started with this powerful new feature, and share customer success stories.

Chronos models are available for Amazon SageMaker customers through AutoGluon-TimeSeries and Amazon SageMaker JumpStart. In this post, we introduce Chronos-Bolt, our latest FM for forecasting that has been integrated into AutoGluon-TimeSeries.

Amazon Finance Automation developed a large language model (LLM)-based question-answer chat assistant on Amazon Bedrock. This solution empowers analysts to rapidly retrieve answers to customer queries, generating prompt responses within the same communication thread. As a result, it drastically reduces the time required to address customer queries. In this post, we share how Amazon Finance Automation built this generative AI Q&A chat assistant using Amazon Bedrock.

We are excited to announce the availability of Cohere’s advanced reranking model Rerank 3.5 through our new Rerank API in Amazon Bedrock. This powerful reranking model enables AWS customers to significantly improve their search relevance and content ranking capabilities. In this post, we discuss the need for Reranking, the capabilities of Cohere’s Rerank 3.5, and how to get started using it on Amazon Bedrock.

Today, we are excited to announce the availability of Prompt Optimization on Amazon Bedrock. With this capability, you can now optimize your prompts for several use cases with a single API call or a click of a button on the Amazon Bedrock console. In this post, we discuss how you can get started with this new feature using an example use case in addition to discussing some performance benchmarks.

In this post, we present a generative AI-powered semantic search solution that empowers business users to quickly and accurately find relevant data assets across various enterprise data sources. In this solution, we integrate large language models (LLMs) hosted on Amazon Bedrock backed by a knowledge base that is derived from a knowledge graph built on Amazon Neptune to create a powerful search paradigm that enables natural language-based questions to integrate search across documents stored in Amazon Simple Storage Service (Amazon S3), data lake tables hosted on the AWS Glue Data Catalog, and enterprise assets in Amazon DataZone.