Data is your generative AI differentiator, and successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Traditional data architectures often struggle to meet the unique demands of generative such as applications. An effective generative AI data strategy requires several key components like seamless integration of diverse data sources, … Read more
This post provides the theoretical foundation and practical insights needed to navigate the complexities of LLM development on Amazon SageMaker AI, helping organizations make optimal choices for their specific use cases, resource constraints, and business objectives. We also address the three fundamental aspects of LLM development: the core lifecycle stages, the spectrum of fine-tuning methodologies, … Read more
This post is co-written with Zhanghao Wu, co-creator of SkyPilot. The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity remains high. Organizations need infrastructure solutions … Read more
Extracting information from unstructured documents at scale is a recurring business task. Common use cases include creating product feature tables from descriptions, extracting metadata from documents, and analyzing legal contracts, customer reviews, news articles, and more. A classic approach to extracting information from text is named entity recognition (NER). NER identifies entities from predefined categories, … Read more
In Part 1 of this series, we explored how Amazon’s Worldwide Returns & ReCommerce (WWRR) organization built the Returns & ReCommerce Data Assist (RRDA)—a generative AI solution that transforms natural language questions into validated SQL queries using Amazon Bedrock Agents. Although this capability improves data access for technical users, the WWRR organization’s journey toward truly … Read more
What if you could replace hours of data analysis with a minute-long conversation? Large language models can transform how we bridge the gap between business questions and actionable data insights. For most organizations, this gap remains stubbornly wide, with business teams trapped in endless cycles—decoding metric definitions and hunting for the correct data sources to … Read more
Managing access control in enterprise machine learning (ML) environments presents significant challenges, particularly when multiple teams share Amazon SageMaker AI resources within a single Amazon Web Services (AWS) account. Although Amazon SageMaker Studio provides user-level execution roles, this approach becomes unwieldy as organizations scale and team sizes grow. Refer to the Operating model whitepaper for … Read more
Today, we announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services together to build and scale predefined generative AI workflows. As customers … Read more
Fraud detection remains a significant challenge in the financial industry, requiring advanced machine learning (ML) techniques to detect fraudulent patterns while maintaining compliance with strict privacy regulations. Traditional ML models often rely on centralized data aggregation, which raises concerns about data security and regulatory constraints. Fraud cost businesses over $485.6 billion in 2023 alone, according … Read more
Voice AI is changing the way we use technology, allowing for more natural and intuitive conversations. Meanwhile, advanced AI agents can now understand complex questions and act autonomously on our behalf. In Part 1 of this series, you learned how you can use the combination of Amazon Bedrock and Pipecat, an open source framework for … Read more
The global fashion industry is estimated to be valued at $1.84 trillion in 2025, accounting for approximately 1.63% of the world’s GDP (Statista, 2025). With such massive amounts of generated capital, so too comes the enormous potential for toxic content and misuse. In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing … Read more
As AI models become increasingly sophisticated and specialized, the ability to quickly train and customize models can mean the difference between industry leadership and falling behind. That is why hundreds of thousands of customers use the fully managed infrastructure, tools, and workflows of Amazon SageMaker AI to scale and advance AI model development. Since launching … Read more
Amazon SageMaker HyperPod now provides a comprehensive, out-of-the-box dashboard that delivers insights into foundation model (FM) development tasks and cluster resources. This unified observability solution automatically publishes key metrics to Amazon Managed Service for Prometheus and visualizes them in Amazon Managed Grafana dashboards, optimized specifically for FM development with deep coverage of hardware health, resource … Read more
Amazon SageMaker now offers fully managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your generative AI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development. As customers across industries accelerate their generative AI development, they require capabilities to … Read more
Today, we’re excited to announce that Amazon SageMaker HyperPod now supports deploying foundation models (FMs) from Amazon SageMaker JumpStart, as well as custom or fine-tuned models from Amazon S3 or Amazon FSx. With this launch, you can train, fine-tune, and deploy models on the same HyperPod compute resources, maximizing resource utilization across the entire model … Read more
AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resources and your data in SageMaker … Read more
As Kubernetes clusters grow in complexity, managing them efficiently becomes increasingly challenging. Troubleshooting modern Kubernetes environments requires deep expertise across multiple domains—networking, storage, security, and the expanding ecosystem of CNCF plugins. With Kubernetes now hosting mission-critical workloads, rapid issue resolution has become paramount to maintaining business continuity. Integrating advanced generative AI tools like K8sGPT and … Read more
Rocket Companies is a Detroit-based FinTech company with a mission to “Help Everyone Home.” Although known to many as a mortgage lender, Rocket’s mission extends to the entire home ownership journey from finding the perfect home, purchasing, financing, and using your home equity. Rocket has grown by making the complex simple, empowering clients to navigate … Read more
This post is cowritten with Siddhant Waghjale and Samuel Barry from Mistral AI. Model Context Protocol (MCP) is a standard that has been gaining significant traction in recent months. At a high level, it consists of a standardized interface designed to streamline and enhance how AI models interact with external data sources and systems. Instead … Read more
The rapid growth of generative AI technology has been a catalyst for business productivity growth, creating new opportunities for greater efficiency, enhanced customer service experiences, and more successful customer outcomes. Today’s generative AI advances are helping existing technologies achieve their long-promised potential. For example, voice-first applications have been gaining traction across industries for years—from customer … Read more
Imagine a system that can explore multiple approaches to complex problems, drawing on its understanding of vast amounts of data, from scientific datasets to source code to business documents, and reasoning through the possibilities in real time. This lightning-fast reasoning isn’t waiting on the horizon. It’s happening today in our customers’ AI production environments. The … Read more
Businesses often face challenges in managing and deriving value from their data. According to McKinsey, 78% of organizations now use AI in at least one business function (as of 2024), showing the growing importance of AI solutions in business. Additionally, 21% of organizations using generative AI have fundamentally redesigned their workflows, showing how AI is … Read more
This post was co-written with Le Vy from Parcel Perform. Access to accurate data is often the true differentiator of excellent and timely decisions. This is even more crucial for customer-facing decisions and actions. A correctly implemented state-of-the-art AI can help your organization simplify access to data for accurate and timely decision-making for the customer-facing … Read more
Amazon Bedrock Knowledge Bases offers a fully managed Retrieval Augmented Generation (RAG) feature that connects large language models (LLMs) to internal data sources. This feature enhances foundation model (FM) outputs with contextual information from private data, making responses more relevant and accurate. At AWS re:Invent 2024, we announced Amazon Bedrock Knowledge Bases support for natural … Read more
Enterprises adopting advanced AI solutions recognize that robust security and precise access control are essential for protecting valuable data, maintaining compliance, and preserving user trust. As organizations expand AI usage across teams and applications, they require granular permissions to safeguard sensitive information and manage who can access powerful models. Amazon SageMaker Unified Studio addresses these … Read more
Many enterprises are using large language models (LLMs) in Amazon Bedrock to gain insights from their internal data sources. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, … Read more
Generative AI continues to reshape how businesses approach innovation and problem-solving. Customers are moving from experimentation to scaling generative AI use cases across their organizations, with more businesses fully integrating these technologies into their core processes. This evolution spans across lines of business (LOBs), teams, and software as a service (SaaS) providers. Although many AWS … Read more
Today, we’re excited to announce a significant improvement to the developer experience of Amazon Bedrock: API keys. API keys provide quick access to the Amazon Bedrock APIs, streamlining the authentication process so that developers can focus on building rather than configuration. CamelAI is an open-source, modular framework for building intelligent multi-agent systems for data generation, … Read more
The world’s population is expanding at a rapid rate. The growing global population requires innovative solutions to produce food, fiber, and fuel, while restoring natural resources like soil and water and addressing climate change. Bayer Crop Science estimates farmers need to increase crop production by 50% by 2050 to meet these demands. To support their … Read more
Financial fraud detection isn’t just important to banks—it’s essential. With global fraud losses surpassing $40 billion annually and sophisticated criminal networks constantly evolving their tactics, financial institutions face an increasingly complex threat landscape. Today’s fraud schemes operate across multiple accounts, institutions, and channels, creating intricate webs designed specifically to evade detection systems. Financial institutions have … Read more