A Technology Broker and Management Company Specializing in KM Systems
Favorite Large language models (LLMs) have demonstrated promising capabilities in machine translation (MT) tasks. Depending on the use case, they are able to compete with neural translation models such as Amazon Translate. LLMs particularly stand out for their natural ability to learn from the context of the input text, which
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Shared by AWS Machine Learning January 8, 2025
Favorite This post was co-written with Ben Doughton, Head of Product Operations – LCH, Iulia Midus, Site Reliability Engineer – LCH, and Maurizio Morabito, Software and AI specialist – LCH (part of London Stock Exchange Group, LSEG). In the financial industry, quick and reliable access to information is essential, but
Read More Shared by AWS Machine Learning January 8, 2025
Favorite Generative AI applications are gaining widespread adoption across various industries, including regulated industries such as financial services and healthcare. As these advanced systems accelerate in playing a critical role in decision-making processes and customer interactions, customers should work towards ensuring the reliability, fairness, and compliance of generative AI applications
Read More Shared by AWS Machine Learning January 8, 2025
Favorite In this post, we walk you through the process to build an automated mechanism using Amazon SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the Amazon SageMaker Model Registry for your customers to use it.
Read More Shared by AWS Machine Learning January 7, 2025
Favorite The report The economic potential of generative AI: The next productivity frontier, published by McKinsey & Company, estimates that generative AI could add an equivalent of $2.6 trillion to $4.4 trillion in value to the global economy. The largest value will be added across four areas: customer operations, marketing
Read More Shared by AWS Machine Learning December 27, 2024
Favorite Training large language models (LLMs) models has become a significant expense for businesses. For many use cases, companies are looking to use LLM foundation models (FM) with their domain-specific data. However, companies are discovering that performing full fine tuning for these models with their data isn’t cost effective. To reduce
Read More Shared by AWS Machine Learning December 25, 2024
Favorite Customers need better accuracy to take generative AI applications into production. In a world where decisions are increasingly data-driven, the integrity and reliability of information are paramount. To address this, customers often begin by enhancing generative AI accuracy through vector-based retrieval systems and the Retrieval Augmented Generation (RAG) architectural
Read More Shared by AWS Machine Learning December 24, 2024
Favorite When building voice-enabled chatbots with Amazon Lex, one of the biggest challenges is accurately capturing user speech input for slot values. For example, when a user needs to provide their account number or confirmation code, speech recognition accuracy becomes crucial. This is where transcription confidence scores come in to
Read More Shared by AWS Machine Learning December 24, 2024
Favorite As generative AI capabilities evolve, successful business adoptions hinge on the development of robust problem-solving capabilities. At the forefront of this transformation are agentic systems, which harness the power of foundation models (FMs) to tackle complex, real-world challenges. By seamlessly integrating multiple agents, these innovative solutions enable autonomous collaboration,
Read More Shared by AWS Machine Learning December 20, 2024
Favorite Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of linguistic tasks. However, the performance of these models is heavily influenced by the data used during the training process. In this blog post, we provide an introduction to preparing your own dataset for LLM training. Whether your
Read More Shared by AWS Machine Learning December 20, 2024