Category: Learning Levels

In this post, we demonstrate how to deploy a generative AI application that can accelerate your financial statement analysis on AWS.

In this post, you will learn how to extract key objects from image queries using Amazon Rekognition and build a reverse image search engine using Amazon Titan Multimodal Embeddings from Amazon Bedrock in combination with Amazon OpenSearch Serverless Service.

In this post, we demonstrate a solution using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Developer Experience, and Amazon Personalize that allow marketers to save time and deliver efficient personalized advertising using a generative AI enhanced solution. Our solution is a marketing agent that shows how Amazon Personalize can effectively segment target customers based on relevant characteristics and behaviors. Additionally, by using Amazon Bedrock Agents and foundation models (FMs), our tool generates personalized creative content specifically tailored to each purpose. It customizes the tone, creative style, and individual preferences according to each customer’s specific prompt, providing highly customized and effective marketing communications.

In this post, we demonstrate how to fine-tune Meta’s latest Llama 3.2 text generation models, Llama 3.2 1B and 3B, using Amazon SageMaker JumpStart for domain-specific applications. By using the pre-built solutions available in SageMaker JumpStart and the customizable Meta Llama 3.2 models, you can unlock the models’ enhanced reasoning, code generation, and instruction-following capabilities to tailor them for your unique use cases.

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 […]

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.

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.