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 Learn more about Google DeepMind’s Project Genie and how to write prompts to create your own worlds. View Original Source (blog.google/technology/ai/) Here.
Favorite Gemini 3.1 Flash-Lite is our fastest and most cost-efficient Gemini 3 series model yet. View Original Source (blog.google/technology/ai/) Here.
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
Favorite Modern large language model (LLM) deployments face an escalating cost and performance challenge driven by token count growth. Token count, which is directly related to word count, image size, and other input factors, determines both computational requirements and costs. Longer contexts translate to higher expenses per inference request. This
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Shared by AWS Machine Learning February 27, 2026
Favorite Foundation models deliver impressive out-of-the-box performance for general tasks, but many organizations need models to consume their business knowledge. Model customization helps you bridge the gap between general-purpose AI and your specific business needs when building applications that require domain-specific expertise, enforcing communication styles, optimizing for specialized tasks like
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Shared by AWS Machine Learning February 27, 2026
Shared by AWS Machine Learning March 4, 2026
