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...
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...
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...
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...
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...
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...
