Technical How-to

Configure fine-grained access to Amazon Bedrock models using Amazon SageMaker Unified Studio

Amazon Bedrock, Amazon Machine Learning, Amazon SageMaker Unified Studio, Artificial Intelligence, AWS Identity and Access Management (IAM), Security, Identity, & Compliance, Technical How-to / aiepicentre

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 […]

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Combat financial fraud with GraphRAG on Amazon Bedrock Knowledge Bases

Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Machine Learning, Amazon Neptune Analytics, Technical How-to / aiepicentre

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

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Qwen3 family of reasoning models now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

Advanced (300), Amazon Bedrock, Amazon SageMaker, Amazon SageMaker JumpStart, Announcements, Featured, Technical How-to / aiepicentre

Today, we are excited to announce that Qwen3, the latest generation of large language models (LLMs) in the Qwen family, is available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Qwen3 models—available in 0.6B, 4B, 8B, and 32B parameter sizes—to build, experiment, and responsibly scale your generative AI

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End-to-End model training and deployment with Amazon SageMaker Unified Studio

Advanced (300), AIML, Amazon SageMaker, Amazon SageMaker Unified Studio, Foundation models, Generative AI, Technical How-to / aiepicentre

Although rapid generative AI advancements are revolutionizing organizational natural language processing tasks, developers and data scientists face significant challenges customizing these large models. These hurdles include managing complex workflows, efficiently preparing large datasets for fine-tuning, implementing sophisticated fine-tuning techniques while optimizing computational resources, consistently tracking model performance, and achieving reliable, scalable deployment.The fragmented nature of

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Use Amazon SageMaker Unified Studio to build complex AI workflows using Amazon Bedrock Flows

Amazon Bedrock, Amazon SageMaker Unified Studio, Intermediate (200), Technical How-to / aiepicentre

Organizations face the challenge to manage data, multiple artificial intelligence and machine learning (AI/ML) tools, and workflows across different environments, impacting productivity and governance. A unified development environment consolidates data processing, model development, and AI application deployment into a single system. This integration streamlines workflows, enhances collaboration, and accelerates AI solution development from concept to

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Accelerating AI innovation: Scale MCP servers for enterprise workloads with Amazon Bedrock

Amazon Bedrock, Amazon DynamoDB, Amazon Elastic Container Service, Artificial Intelligence, AWS Fargate, AWS PrivateLink, Elastic Load Balancing, Financial Services, Generative AI, Technical How-to / aiepicentre

Generative AI has been moving at a rapid pace, with new tools, offerings, and models released frequently. According to Gartner, agentic AI is one of the top technology trends of 2025, and organizations are performing prototypes on how to use agents in their enterprise environment. Agents depend on tools, and each tool might have its

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Build AWS architecture diagrams using Amazon Q CLI and MCP

AI/ML, Amazon Q Developer, Best Practices, Generative AI, Technical How-to / aiepicentre

Creating professional AWS architecture diagrams is a fundamental task for solutions architects, developers, and technical teams. These diagrams serve as essential communication tools for stakeholders, documentation of compliance requirements, and blueprints for implementation teams. However, traditional diagramming approaches present several challenges: Time-consuming process – Creating detailed architecture diagrams manually can take hours or even days

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AWS costs estimation using Amazon Q CLI and AWS Cost Analysis MCP

Amazon Q Developer, Best Practices, Generative AI, Technical How-to / aiepicentre

Managing and optimizing AWS infrastructure costs is a critical challenge for organizations of all sizes. Traditional cost analysis approaches often involve the following: Complex spreadsheets – Creating and maintaining detailed cost models, which requires significant effort Multiple tools – Switching between the AWS Pricing Calculator, AWS Cost Explorer, and third-party tools Specialized knowledge – Understanding

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Tailor responsible AI with new safeguard tiers in Amazon Bedrock Guardrails

Amazon Bedrock, Amazon Bedrock Guardrails, Artificial Intelligence, Best Practices, Generative AI, Responsible AI, Technical How-to, Thought Leadership / aiepicentre

Amazon Bedrock Guardrails provides configurable safeguards to help build trusted generative AI applications at scale. It provides organizations with integrated safety and privacy safeguards that work across multiple foundation models (FMs), including models available in Amazon Bedrock, as well as models hosted outside Amazon Bedrock from other model providers and cloud providers. With the standalone

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Structured data response with Amazon Bedrock: Prompt Engineering and Tool Use

Amazon Bedrock, Best Practices, Generative AI, Intermediate (200), Technical How-to / aiepicentre

Generative AI is revolutionizing industries by streamlining operations and enabling innovation. While textual chat interactions with GenAI remain popular, real-world applications often depend on structured data for APIs, databases, data-driven workloads, and rich user interfaces. Structured data can also enhance conversational AI, enabling more reliable and actionable outputs. A key challenge is that LLMs (Large

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