Category: Generative AI

To stay competitive, media, advertising, and entertainment enterprises need to stay abreast of recent dramatic technological developments. Generative AI has emerged as a game-changer, offering unprecedented opportunities for creative professionals to push boundaries and unlock new realms of possibility. At the forefront of this revolution is Stability AI’s  family of cutting-edge text-to-image AI models. These […]

While organizations continue to discover the powerful applications of generative AI, adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. In the first part of the series, we showed how AI administrators can build a […]

Generative AI has transformed customer support, offering businesses the ability to respond faster, more accurately, and with greater personalization. AI agents, powered by large language models (LLMs), can analyze complex customer inquiries, access multiple data sources, and deliver relevant, detailed responses. In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data […]

In this post, we share how Hearst, one of the nation’s largest global, diversified information, services, and media companies, overcame these challenges by creating a self-service generative AI conversational assistant for business units seeking guidance from their CCoE.

The rise of large language models (LLMs) and foundation models (FMs) has revolutionized the field of natural language processing (NLP) and artificial intelligence (AI). These powerful models, trained on vast amounts of data, can generate human-like text, answer questions, and even engage in creative writing tasks. However, training and deploying such models from scratch is […]

AWS provides a powerful set of tools and services that simplify the process of building and deploying generative AI applications, even for those with limited experience in frontend and backend development. In this post, we explore a practical solution that uses Streamlit, a Python library for building interactive data applications, and AWS services like Amazon Elastic Container Service (Amazon ECS), Amazon Cognito, and the AWS Cloud Development Kit (AWS CDK) to create a user-friendly generative AI application with authentication and deployment.

In this post, we examine how to create business value through speech analytics with some examples focused on the following: 1) automatically summarizing, categorizing, and analyzing marketing content such as podcasts, recorded interviews, or videos, and creating new marketing materials based on those assets, 2) automatically extracting key points, summaries, and sentiment from a recorded meeting (such as an earnings call), and 3) transcribing and analyzing contact center calls to improve customer experience.

In this post, we explore the best practices and lessons learned for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock. We discuss the important components of fine-tuning, including use case definition, data preparation, model customization, and performance evaluation.

Amazon Bedrock has launched a capability that organizations can use to tag on-demand models and monitor associated costs. Organizations can now label all Amazon Bedrock models with AWS cost allocation tags, aligning usage to specific organizational taxonomies such as cost centers, business units, and applications.

This post covers the steps to configure the Amazon Q Business Google Drive connector, including authentication setup and verifying the secure indexing of your Google Drive content.