Best Practices

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Beyond the basics: A comprehensive foundation model selection framework for generative AI

Amazon Bedrock, Artificial Intelligence, Best Practices, Foundation models, Generative AI, Thought Leadership /

Most organizations evaluating foundation models limit their analysis to three primary dimensions: accuracy, latency, and cost. While these metrics provide a useful starting point, they represent an oversimplification of the complex interplay of factors that determine real-world model performance. Foundation models have revolutionized how enterprises develop generative AI applications, offering unprecedented capabilities in understanding and […]

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Accelerate enterprise AI implementations with Amazon Q Business

AI/ML, Amazon Q Business, Artificial Intelligence, Best Practices, Generative AI /

As an Amazon Web Services (AWS) enterprise customer, you’re probably exploring ways to use generative AI to enhance your business processes, improve customer experiences, and drive innovation. With a variety of options available—from Amazon Q Business to other AWS services or third-party offerings—choosing the right tool for your use case can be challenging. This post

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Responsible AI for the payments industry – Part 1

Best Practices, Financial Services, Intermediate (200), Responsible AI /

The payments industry stands at the forefront of digital transformation, with artificial intelligence (AI) rapidly becoming a cornerstone technology that powers a variety of solutions, from fraud detection to customer service. According to the following Number Analytics report, digital payment transactions are projected to exceed $15 trillion globally by 2027. Generative AI has expanded the

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Responsible AI for the payments industry – Part 2

Best Practices, Financial Services, Industries, Intermediate (200), Responsible AI /

In Part 1 of our series, we explored the foundational concepts of responsible AI in the payments industry. In this post, we discuss the practical implementation of responsible AI frameworks. The need for responsible AI The implementation of responsible AI is not passive, but a dynamic process of reimagining how technology can serve a customer’s

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Structured outputs with Amazon Nova: A guide for builders

AI/ML, Amazon Bedrock, Amazon Nova, Artificial Intelligence, Best Practices, Generative AI, Technical How-to /

Developers building AI applications face a common challenge: converting unstructured data into structured formats. Structured output is critical for machine-to-machine communication use cases, because this enables downstream use cases to more effectively consume and process the generated outputs. Whether it’s extracting information from documents, creating assistants that fetch data from APIs, or developing agents that

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Build an intelligent eDiscovery solution using Amazon Bedrock Agents

Amazon Bedrock, Amazon Bedrock Agents, Best Practices, Customer Solutions, Generative AI, Technical How-to /

Legal teams spend bulk of their time manually reviewing documents during eDiscovery. This process involves analyzing electronically stored information across emails, contracts, financial records, and collaboration systems for legal proceedings. This manual approach creates significant bottlenecks: attorneys must identify privileged communications, assess legal risks, extract contractual obligations, and maintain regulatory compliance across thousands of documents

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Enhance generative AI solutions using Amazon Q index with Model Context Protocol – Part 1

Amazon Q, Amazon Q Business, Artificial Intelligence, Best Practices /

Today’s enterprises increasingly rely on AI-driven applications to enhance decision-making, streamline workflows, and deliver improved customer experiences. Achieving these outcomes demands secure, timely, and accurate access to authoritative data—especially when such data resides across diverse repositories and applications within strict enterprise security boundaries. Interoperable technologies powered by open standards like the Model Context Protocol (MCP)

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Beyond accelerators: Lessons from building foundation models on AWS with Japan’s GENIAC program

AI/ML, Amazon Elastic Kubernetes Service, Amazon Machine Learning, Amazon SageMaker HyperPod, Artificial Intelligence, AWS ParallelCluster, Best Practices /

In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure

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Build secure RAG applications with AWS serverless data lakes

Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker Data & AI Governance, Amazon SageMaker Lakehouse, Amazon Simple Storage Service (S3), Best Practices, Customer Solutions, Generative AI, Technical How-to /

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,

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Advanced fine-tuning methods on Amazon SageMaker AI

Amazon SageMaker AI, Artificial Intelligence, Best Practices /

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,

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