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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 with industry regulations. To address this …

Align and monitor your Amazon Bedrock powered insurance assistance chatbot to responsible AI principles with AWS Audit Manager Read More »

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 searching for data or facing unclear …

London Stock Exchange Group uses Amazon Q Business to enhance post-trade client services Read More »

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 allows them to pick up on …

Evaluate large language models for your machine translation tasks on AWS Read More »

This blog post is co-written with Siokhan Kouassi and Martin Gregory at Parameta.  When financial industry professionals need reliable over-the-counter (OTC) data solutions and advanced analytics, they can turn to Parameta Solutions, the data powerhouse behind TP ICAP . With a focus on data-led solutions, Parameta Solutions makes sure that these professionals have the insights …

Parameta accelerates client email resolution with Amazon Bedrock Flows Read More »

Google Lens uses AI to help you search the world around you. Here’s how to make the most of it.

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. Log-based anomaly detection involves identifying anomalous …

Efficiently build and tune custom log anomaly detection models with Amazon SageMaker Read More »

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 and sales, software engineering, and R&D. …

Optimizing costs of generative AI applications on AWS Read More »

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 costs while continuing to use the …

PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium Read More »

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 help ensure reliable slot filling. What …

Using transcription confidence scores to improve slot filling in Amazon Lex Read More »