Favorite As your conversational AI initiatives evolve, developing Amazon Lex assistants becomes increasingly complex. Multiple developers working on the same shared Lex instance leads to configuration conflicts, overwritten changes, and slower iteration cycles. Scaling Amazon Lex development requires isolated environments, version control, and automated deployment pipelines. By adopting well-structured continuous

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Shared by AWS Machine Learning March 6, 2026

Favorite This post is cowritten by Jeremy Jacobson and Rado Fulek from Ricoh. This post demonstrates how enterprises can overcome document processing scaling limits by combining generative AI, serverless architecture, and standardized frameworks. Ricoh engineered a repeatable, reusable framework using the AWS GenAI Intelligent Document Processing (IDP) Accelerator. This framework

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Shared by AWS Machine Learning March 5, 2026

Favorite Call center analytics play a crucial role in improving customer experience and operational efficiency. With foundation models (FMs), you can improve the quality and efficiency of call center operations and analytics. Organizations can use generative AI to assist human customer support agents and managers of contact center teams, so

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Shared by AWS Machine Learning March 5, 2026

Favorite Organizations can face two critical challenges with conversational AI. First, users need answers where they work—in their CRM, support console, or analytics portal—not in separate tools. Second, implementing a secure embedded chat in their applications can require weeks of development to build authentication, token validation, domain security, and global

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Shared by AWS Machine Learning March 5, 2026

Favorite Organizations face challenges in quickly detecting and responding to user account security events, such as repeated login attempts from unusual locations. Although security data exists across multiple applications, manually correlating information and making corrective actions often delays effective response. With Amazon Quick Suite and Tines, you can automate the

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Shared by AWS Machine Learning March 4, 2026

Favorite This post was co-written with Davesh Maheshwari from Lendi Group and Samuel Casey from Mantel Group. Most Australians don’t know whether their home loan is still competitive. Rates shift, property values move, personal circumstances change—yet for the average homeowner, staying informed of these changes is difficult. It’s often their

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Shared by AWS Machine Learning March 4, 2026

Favorite In this first post in a two-part series, we examine how retailers can implement a virtual try-on to improve customer experience. In part 2, we will further explore real-world applications and benefits of this innovative technology. Every fourth piece of clothing bought online is returned to the retailer, feeding

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Shared by AWS Machine Learning March 4, 2026

Favorite Are you struggling to balance generative AI safety with accuracy, performance, and costs? Many organizations face this challenge when deploying generative AI applications to production. A guardrail that’s too strict blocks legitimate user requests, which frustrates customers. One that’s too lenient exposes your application to harmful content, prompt attacks,

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Shared by AWS Machine Learning March 3, 2026

Favorite Customer service teams face a persistent challenge. Existing chat-based assistants frustrate users with rigid responses, while direct large language model (LLM) implementations lack the structure needed for reliable business operations. When customers need help with order inquiries, cancellations, or status updates, traditional approaches either fail to understand natural language

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Shared by AWS Machine Learning March 3, 2026

Favorite Large language models (LLMs) perform well on general tasks but struggle with specialized work that requires understanding proprietary data, internal processes, and industry-specific terminology. Supervised fine-tuning (SFT) adapts LLMs to these organizational contexts. SFT can be implemented through two distinct methodologies: Parameter-Efficient Fine-Tuning (PEFT), which updates only a subset

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Shared by AWS Machine Learning March 3, 2026