Favorite Embeddings play a key role in natural language processing (NLP) and machine learning (ML). Text embedding refers to the process of transforming text into numerical representations that reside in a high-dimensional vector space. This technique is achieved through the use of ML algorithms that enable the understanding of the

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Shared by AWS Machine Learning February 1, 2024

Favorite Data is the foundation to capturing the maximum value from AI technology and solving business problems quickly. To unlock the potential of generative AI technologies, however, there’s a key prerequisite: your data needs to be appropriately prepared. In this post, we describe how use generative AI to update and

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Shared by AWS Machine Learning February 1, 2024

Favorite Resilience plays a pivotal role in the development of any workload, and generative AI workloads are no different. There are unique considerations when engineering generative AI workloads through a resilience lens. Understanding and prioritizing resilience is crucial for generative AI workloads to meet organizational availability and business continuity requirements.

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Shared by AWS Machine Learning February 1, 2024

Favorite In the first part of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. In this post, we present an approach to develop a

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Shared by AWS Machine Learning January 31, 2024

Favorite This post was co-written with Ricardo Perdigao, Solution Architecture Manager at Mendix, a Siemens business. Mendix, a Siemens business, offers the low-code platform with the vision and execution designed for today’s complex software development challenges. Since 2005, we’ve helped thousands of organizations worldwide reimagine how they develop applications with

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Shared by AWS Machine Learning January 31, 2024

Favorite Improving how users discover new content is critical to increase user engagement and satisfaction on media platforms. Keyword search alone has challenges capturing semantics and user intent, leading to results that lack relevant context; for example, finding date night or Christmas-themed movies. This can drive lower retention rates if

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Shared by AWS Machine Learning January 31, 2024

Favorite With the advent of generative AI, today’s foundation models (FMs), such as the large language models (LLMs) Claude 2 and Llama 2, can perform a range of generative tasks such as question answering, summarization, and content creation on text data. However, real-world data exists in multiple modalities, such as

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Shared by AWS Machine Learning January 30, 2024

Favorite When deploying a large language model (LLM), machine learning (ML) practitioners typically care about two measurements for model serving performance: latency, defined by the time it takes to generate a single token, and throughput, defined by the number of tokens generated per second. Although a single request to the

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Shared by AWS Machine Learning January 29, 2024

Favorite Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. Examples of applications include conversational search, customer support agent assistance, customer support analytics, self-service virtual assistants, chatbots, rich media generation, content moderation, coding companions to accelerate

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Shared by AWS Machine Learning January 27, 2024

Favorite Amazon Q is a new generative AI-powered application that helps users get work done. Amazon Q can become your tailored business expert and let you discover content, brainstorm ideas, or create summaries using your company’s data safely and securely. You can use Amazon Q to have conversations, solve problems,

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Shared by AWS Machine Learning January 25, 2024