Tag: Natural Language Processing

This post demonstrates how to use Amazon Bedrock Agents, Amazon Knowledge Bases, and the RAGAS evaluation metrics to build a custom hallucination detector and remediate it by using human-in-the-loop. The agentic workflow can be extended to custom use cases through different hallucination remediation techniques and offers the flexibility to detect and mitigate hallucinations using custom actions.

In this post, we demonstrate how to use Amazon Bedrock with the AWS SDK for Python (Boto3) to programmatically incorporate FMs. We explore invoking a specific FM and processing the generated text, showcasing the potential for developers to use these models in their applications for a variety of use cases

In this post, we show you how DoorDash built a generative AI agent using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases to provide a low-latency, self-service experience for their delivery workers.

Originally PyTorch used an eager mode where each PyTorch operation that forms the model is run independently as soon as it’s reached. PyTorch 2.0 introduced torch.compile to speed up PyTorch code over the default eager mode. In contrast to eager mode, the torch.compile pre-compiles the entire model into a single graph in a manner that’s optimal for […]

ONNX is an open source machine learning (ML) framework that provides interoperability across a wide range of frameworks, operating systems, and hardware platforms. ONNX Runtime is the runtime engine used for model inference and training with ONNX. AWS Graviton3 processors are optimized for ML workloads, including support for bfloat16, Scalable Vector Extension (SVE), and Matrix […]

Natural language processing (NLP) is the field in machine learning (ML) concerned with giving computers the ability to understand text and spoken words in the same way as human beings can. Recently, state-of-the-art architectures like the transformer architecture are used to achieve near-human performance on NLP downstream tasks like text summarization, text classification, entity recognition, […]

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. One of the main challenges can be deploying a well-performing, locally trained model to the cloud for inference and use in other applications. It can be cumbersome to manage the process, but with the right tool, […]

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers […]

Intelligent document processing (IDP) has seen widespread adoption across enterprise and government organizations. Gartner estimates the IDP market will grow more than 100% year over year, and is projected to reach $4.8 billion in 2022. IDP helps transform structured, semi-structured, and unstructured data from a variety of document formats into actionable information. Processing unstructured data […]

“85% of buyers trust online reviews as much as a personal recommendation” – Gartner Consumers are increasingly engaging with businesses through digital surfaces and multiple touchpoints. Statistics show that the majority of shoppers use reviews to determine what products to buy and which services to use. As per Spiegel Research Centre, the purchase likelihood for […]