Generate financial industry-specific insights using generative AI and in-context fine-tuning

In this blog post, we demonstrate prompt engineering techniques to generate accurate and relevant analysis of tabular data using industry-specific language. This is done by providing large language models (LLMs) in-context sample data with features and labels in the prompt. The results are similar to fine-tuning LLMs without the complexities of fine-tuning models. We used … Read more

Deliver personalized marketing with Amazon Bedrock Agents

Creative content plays a crucial role in marketing, and personalized creative content in particular significantly boosts marketing performance. Generating personalized content can present a significant challenge for marketers because it requires considerable time and resources. This challenge stems from the need for multiple versions of creative content across various channels, such as paid media (ads) … Read more

Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart

Generative AI models have seen tremendous growth, offering cutting-edge solutions for text generation, summarization, code generation, and question answering. Despite their versatility, these models often struggle when applied to niche or domain-specific tasks because their pre-training is typically based on large, generalized datasets. To address these gaps and maximize their utility in specialized scenarios, fine-tuning … Read more

Discover insights with the Amazon Q Business Microsoft Teams connector

Microsoft Teams is an enterprise collaboration tool that allows you to build a unified workspace for real-time collaboration and communication, meetings, and file and application sharing. You can exchange and store valuable organizational knowledge within Microsoft Teams. Microsoft Teams data is often siloed across different teams, channels, and chats, making it difficult to get a … Read more

Amazon Bedrock Prompt Management is now available in GA

Today we are announcing the general availability of Amazon Bedrock Prompt Management, with new features that provide enhanced options for configuring your prompts and enabling seamless integration for invoking them in your generative AI applications. Amazon Bedrock Prompt Management simplifies the creation, evaluation, versioning, and sharing of prompts to help developers and prompt engineers get … Read more

How Zalando optimized large-scale inference and streamlined ML operations on Amazon SageMaker

This post is cowritten with Mones Raslan, Ravi Sharma and Adele Gouttes from Zalando. Zalando SE is one of Europe’s largest ecommerce fashion retailers with around 50 million active customers. Zalando faces the challenge of regular (weekly or daily) discount steering for more than 1 million products, also referred to as markdown pricing. Markdown pricing is … Read more

Unleashing Stability AI’s most advanced text-to-image models for media, marketing and advertising: Revolutionizing creative workflows

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 … Read more

Build a multi-tenant generative AI environment for your enterprise on AWS

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 … Read more

Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

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 … Read more

Unleash the power of generative AI with Amazon Q Business: How CCoEs can scale cloud governance best practices and drive innovation

This post is co-written with Steven Craig from Hearst.  To maintain their competitive edge, organizations are constantly seeking ways to accelerate cloud adoption, streamline processes, and drive innovation. However, Cloud Center of Excellence (CCoE) teams often can be perceived as bottlenecks to organizational transformation due to limited resources and overwhelming demand for their support. In … Read more

Integrate foundation models into your code with Amazon Bedrock

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 … Read more

Build and deploy a UI for your generative AI applications with AWS and Python

The emergence of generative AI has ushered in a new era of possibilities, enabling the creation of human-like text, images, code, and more. However, as exciting as these advancements are, data scientists often face challenges when it comes to developing UIs and to prototyping and interacting with their business users. Traditionally, building frontend and backend … Read more

Unearth insights from audio transcripts generated by Amazon Transcribe using Amazon Bedrock

Generative AI continues to push the boundaries of what’s possible. One area garnering significant attention is the use of generative AI to analyze audio and video transcripts, increasing our ability to extract valuable insights from content stored in audio or video files. Speech data is unique and complex, which makes it difficult to analyze and … Read more

Best practices and lessons for fine-tuning Anthropic’s Claude 3 Haiku on Amazon Bedrock

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI, allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. This process involves updating the model’s weights to improve its performance on targeted applications. By fine-tuning, the LLM can adapt its knowledge base to specific data and tasks, resulting in … Read more

Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

As enterprises increasingly embrace generative AI , they face challenges in managing the associated costs. With demand for generative AI applications surging across projects and multiple lines of business, accurately allocating and tracking spend becomes more complex. Organizations need to prioritize their generative AI spending based on business impact and criticality while maintaining cost transparency … Read more

Advance environmental sustainability in clinical trials using AWS

Traditionally, clinical trials not only place a significant burden on patients and participants due to the costs associated with transportation, lodging, meals, and dependent care, but also have an environmental impact. With the advancement of available technologies, decentralized clinical trials have become a widely popular topic of discussion and offer a more sustainable approach. Decentralized … Read more

Use Amazon Q to find answers on Google Drive in an enterprise

Amazon Q Business is a generative AI-powered assistant designed to enhance enterprise operations. It’s a fully managed service that helps provide accurate answers to users’ questions while adhering to the security and access restrictions of the content. You can tailor Amazon Q Business to your specific business needs by connecting to your company’s information and … Read more

How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

This post is co-written with David Gildea and Tom Nijs from Druva. Druva enables cyber, data, and operational resilience for thousands of enterprises, and is trusted by 60 of the Fortune 500. Customers use Druva Data Resiliency Cloud to simplify data protection, streamline data governance, and gain data visibility and insights. Independent software vendors (ISVs) … Read more

Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS offers powerful generative AI services, including Amazon Bedrock, which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. Many businesses want to integrate these cutting-edge AI capabilities with their existing collaboration tools, such as Google Chat, to … Read more

Discover insights from Gmail using the Gmail connector for Amazon Q Business

A number of organizations use Gmail for their business email needs. Gmail for business is part of Google Workspace, which provides a set of productivity and collaboration tools like Google Drive, Gmail, and Google Calendar. Google Drive supports storing documents such as Emails contain a wealth of information found in different places, such as within … Read more

Accelerate custom labeling workflows in Amazon SageMaker Ground Truth without using AWS Lambda

Amazon SageMaker Ground Truth enables the creation of high-quality, large-scale training datasets, essential for fine-tuning across a wide range of applications, including large language models (LLMs) and generative AI. By integrating human annotators with machine learning, SageMaker Ground Truth significantly reduces the cost and time required for data labeling. Whether it’s annotating images, videos, or … Read more

Unlock organizational wisdom using voice-driven knowledge capture with Amazon Transcribe and Amazon Bedrock

Preserving and taking advantage of institutional knowledge is critical for organizational success and adaptability. This collective wisdom, comprising insights and experiences accumulated by employees over time, often exists as tacit knowledge passed down informally. Formalizing and documenting this invaluable resource can help organizations maintain institutional memory, drive innovation, enhance decision-making processes, and accelerate onboarding for … Read more

Achieve multi-Region resiliency for your conversational AI chatbots with Amazon Lex

Global Resiliency is a new Amazon Lex capability that enables near real-time replication of your Amazon Lex V2 bots in a second AWS Region. When you activate this feature, all resources, versions, and aliases associated after activation will be synchronized across the chosen Regions. With Global Resiliency, the replicated bot resources and aliases in the … Read more

Create and fine-tune sentence transformers for enhanced classification accuracy

Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. In this post, we showcase how to fine-tune a sentence transformer specifically for classifying an Amazon … Read more

Empower your generative AI application with a comprehensive custom observability solution

Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics. Evaluation, on the other hand, involves … Read more

Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and … Read more

Build a video insights and summarization engine using generative AI with Amazon Bedrock

Professionals in a wide variety of industries have adopted digital video conferencing tools as part of their regular meetings with suppliers, colleagues, and customers. These meetings often involve exchanging information and discussing actions that one or more parties must take after the session. The traditional way to make sure information and actions aren’t forgotten is … Read more

Automate document processing with Amazon Bedrock Prompt Flows (preview)

Enterprises in industries like manufacturing, finance, and healthcare are inundated with a constant flow of documents—from financial reports and contracts to patient records and supply chain documents. Historically, processing and extracting insights from these unstructured data sources has been a manual, time-consuming, and error-prone task. However, the rise of intelligent document processing (IDP), which uses … Read more

Governing the ML lifecycle at scale: Centralized observability with Amazon SageMaker and Amazon CloudWatch

This post is part of an ongoing series on governing the machine learning (ML) lifecycle at scale. To start from the beginning, refer to Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker. A multi-account strategy is essential not only for improving governance but also for enhancing … Read more

Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas

In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This post presents an architectural approach to extract data from different cloud environments, such as Google Cloud Platform (GCP) BigQuery, without … Read more