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Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. Data scientists face numerous challenges throughout this process, such as selecting appropriate tools, needing step-by-step instructions with code samples, and troubleshooting errors and issues. These iterative challenges can hinder progress and slow down projects. …

Accelerate development of ML workflows with Amazon Q Developer in Amazon SageMaker Studio Read More »

With the rise of powerful foundation models (FMs) powered by services such as Amazon Bedrock and Amazon SageMaker JumpStart, enterprises want to exercise granular control over which users and groups can access and use these models. This is crucial for compliance, security, and governance. Launched in 2021, Amazon SageMaker Canvas is a visual point-and-click service …

Govern generative AI in the enterprise with Amazon SageMaker Canvas Read More »

This post is co-written with Josh Zook and Alex Hamilton from Rocket Mortgage. Rocket Mortgage, America’s largest retail mortgage lender, revolutionizes homeownership with Rocket Logic – Synopsis, an AI tool built on AWS.  This innovation has transformed client interactions and operational efficiency through the use of Amazon Transcribe Call Analytics, Amazon Comprehend, and Amazon Bedrock. …

Transforming home ownership with Amazon Transcribe Call Analytics, Amazon Comprehend, and Amazon Bedrock: Rocket Mortgage’s journey with AWS Read More »

The UN is expanding the integration of Google’s Data Commons into a dozen additional agencies.

Editor’s note: This week in New York City, leaders from around the world are gathering for the 79th United Nations General Assembly (UNGA) — including the first ever “S…

Amazon Bedrock Agents offers developers the ability to build and configure autonomous agents in their applications. These agents help users complete actions based on organizational data and user input, orchestrating interactions between foundation models (FMs), data sources, software applications, and user conversations. Amazon Bedrock agents use the power of large language models (LLMs) to perform …

Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents Read More »

In today’s fast-paced business environment, organizations are constantly seeking innovative ways to enhance employee experience and productivity. There are many challenges that can impact employee productivity, such as cumbersome search experiences or finding specific information across an organization’s vast knowledge bases. Additionally, with the rise of remote and hybrid work models, traditional support systems such …

Build a generative AI assistant to enhance employee experience using Amazon Q Business Read More »

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra helps you aggregate content from a variety of content repositories into a centralized index that lets you quickly search all your enterprise data and find the most accurate answer. Amazon Kendra securely connects to over 40 data sources. When using your data …

Introducing document-level sync reports: Enhanced data sync visibility in Amazon Kendra Read More »

This post is co-written with Meta’s PyTorch team. In today’s rapidly evolving AI landscape, businesses are constantly seeking ways to use advanced large language models (LLMs) for their specific needs. Although foundation models (FMs) offer impressive out-of-the-box capabilities, true competitive advantage often lies in deep model customization through fine-tuning. However, fine-tuning LLMs for complex tasks …

Fine-tune Meta Llama 3.1 models using torchtune on Amazon SageMaker Read More »