Category: Amazon Machine Learning

In Parts 1 and 2 of this series, we explored ways to use the power of multimodal FMs such as Amazon Titan Multimodal Embeddings, Amazon Titan Text Embeddings, and Anthropic’s Claude 3 Sonnet. In this post, we compared the approaches from an accuracy and pricing perspective.

With trust as a cornerstone of AI adoption, we are excited to announce at AWS re:Invent 2024 new responsible AI tools, capabilities, and resources that enhance the safety, security, and transparency of our AI services and models and help support customers’ own responsible AI journeys.

At AWS re:Invent 2024, we are excited to introduce Amazon Bedrock Marketplace. This a revolutionary new capability within Amazon Bedrock that serves as a centralized hub for discovering, testing, and implementing foundation models (FMs). In this post, we discuss the advantages and capabilities of Amazon Bedrock Marketplace and Nemotron models, and how to get started.

In this post, we explore how to deploy AI models from SageMaker JumpStart and use them with Amazon Bedrock’s powerful features. Users can combine SageMaker JumpStart’s model hosting with Bedrock’s security and monitoring tools. We demonstrate this using the Gemma 2 9B Instruct model as an example, showing how to deploy it and use Bedrock’s advanced capabilities.

This post introduces the workflow of Amazon Bedrock Model Distillation. We first introduce the general concept of model distillation in Amazon Bedrock, and then focus on the important steps in model distillation, including setting up permissions, selecting the models, providing input dataset, commencing the model distillation jobs, and conducting evaluation and deployment of the student models after model distillation.

In this post, we’ll show how anyone in your company can use Amazon Bedrock IDE to quickly create a generative AI chat agent application that analyzes sales performance data. Through simple conversations, business teams can use the chat agent to extract valuable insights from both structured and unstructured data sources without writing code or managing complex data pipelines.

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.

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.