AWS Machine Learning Blog
Category: Artificial Intelligence
Train self-supervised vision transformers on overhead imagery with Amazon SageMaker
In this post, we demonstrate how to train self-supervised vision transformers on overhead imagery using Amazon SageMaker. Travelers collaborated with the Amazon Machine Learning Solutions Lab (now known as the Generative AI Innovation Center) to develop this framework to support and enhance aerial imagery model use cases.
How Thomson Reuters developed Open Arena, an enterprise-grade large language model playground, in under 6 weeks
In this post, we discuss how Thomson Reuters Labs created Open Arena, Thomson Reuters’s enterprise-wide large language model (LLM) playground that was developed in collaboration with AWS. The original concept came out of an AI/ML Hackathon supported by Simone Zucchet (AWS Solutions Architect) and Tim Precious (AWS Account Manager) and was developed into production using AWS services in under 6 weeks with support from AWS. AWS-managed services such as AWS Lambda, Amazon DynamoDB, and Amazon SageMaker, as well as the pre-built Hugging Face Deep Learning Containers (DLCs), contributed to the pace of innovation.
How Amazon Shopping uses Amazon Rekognition Content Moderation to review harmful images in product reviews
Customers are increasingly turning to product reviews to make informed decisions in their shopping journey, whether they’re purchasing everyday items like a kitchen towel or making major purchases like buying a car. These reviews have transformed into an essential source of information, enabling shoppers to access the opinions and experiences of other customers. As a […]
Intelligent video and audio Q&A with multilingual support using LLMs on Amazon SageMaker
Digital assets are vital visual representations of products, services, culture, and brand identity for businesses in an increasingly digital world. Digital assets, together with recorded user behavior, can facilitate customer engagement by offering interactive and personalized experiences, allowing companies to connect with their target audience on a deeper level. Efficiently discovering and searching for specific […]
Zero-shot and few-shot prompting for the BloomZ 176B foundation model with the simplified Amazon SageMaker JumpStart SDK
Amazon SageMaker JumpStart is a machine learning (ML) hub offering algorithms, models, and ML solutions. With SageMaker JumpStart, ML practitioners can choose from a growing list of best performing and publicly available foundation models (FMs) such as BLOOM, Llama 2, Falcon-40B, Stable Diffusion, OpenLLaMA, Flan-T5/UL2, or FMs from Cohere and LightOn. In this post and […]
Build production-ready generative AI applications for enterprise search using Haystack pipelines and Amazon SageMaker JumpStart with LLMs
In this post, we showcase how to build an end-to-end generative AI application for enterprise search with Retrieval Augmented Generation (RAG) by using Haystack pipelines and the Falcon-40b-instruct model from Amazon SageMaker JumpStart and Amazon OpenSearch Service.
Amazon Translate enhances its custom terminology to improve translation accuracy and fluency
Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation. When you translate from one language to another, you want your machine translation to be accurate, fluent, and most importantly contextual. Domain-specific and language-specific customizable terminology is a key requirement for many government and commercial organizations. Custom terminology […]
Zero-shot text classification with Amazon SageMaker JumpStart
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, […]
Build a centralized monitoring and reporting solution for Amazon SageMaker using Amazon CloudWatch
In this post, we present a cross-account observability dashboard that provides a centralized view for monitoring SageMaker user activities and resources across multiple accounts. It allows the end-users and cloud management team to efficiently monitor what ML workloads are running, view the status of these workloads, and trace back different account activities at certain points of time.
Generate creative advertising using generative AI deployed on Amazon SageMaker
Creative advertising has the potential to be revolutionized by generative AI (GenAI). You can now create a wide variation of novel images, such as product shots, by retraining a GenAI model and providing a few inputs into the model, such as textual prompts (sentences describing the scene and objects to be produced by the model). […]