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...

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,...

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...

Natural language generation (NLG) is an enthralling area of artificial intelligence (AI) , or more specifically of natural language processing (NLP) , aimed at enabling machines to produce human-like text that drives human-machine communication for problem-solving.

In real life, the machine learning model is not a standalone object that only produces a prediction.

This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems.