AWS Machine Learning Blog

We introduced Amazon Bedrock to the world a little over a year ago, delivering an entirely new way to build generative artificial intelligence (AI) applications. With the broadest selection of first- and third-party foundation models (FMs) as well as user-friendly capabilities, Amazon Bedrock is the fastest and easiest way to build and scale secure generative […]

Amazon SageMaker JumpStart provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including […]

The telecommunications industry is more competitive than ever before. With customers able to easily switch between providers, reducing customer churn is a crucial priority for telecom companies who want to stay ahead. To address this challenge, Dialog Axiata has pioneered a cutting-edge solution called the Home Broadband (HBB) Churn Prediction Model. This post explores the […]

Amazon SageMaker is a fully managed machine learning (ML) service that provides a range of tools and features for building, training, and deploying ML models. Amazon DataZone is a data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources. Today, […]

This post presents a solution to automatically generate a meeting summary from a recorded virtual meeting (for example, using Amazon Chime) with several participants. The recording is transcribed to text using Amazon Transcribe and then processed using Amazon SageMaker Hugging Face containers to generate the meeting summary. The Hugging Face containers host a large language model (LLM) from the Hugging Face Hub.

This post is co-written with Tim Camara, Senior Product Manager at Veritone. Veritone is an artificial intelligence (AI) company based in Irvine, California. Founded in 2014, Veritone empowers people with AI-powered software and solutions for various applications, including media processing, analytics, advertising, and more. It offers solutions for media transcription, facial recognition, content summarization, object […]

Large language models (LLMs) have unlocked new possibilities for extracting information from unstructured text data. Although much of the current excitement is around LLMs for generative AI tasks, many of the key use cases that you might want to solve have not fundamentally changed. Tasks such as routing support tickets, recognizing customers intents from a […]

Today, we’re excited to announce the availability of Meta Llama 3 inference on AWS Trainium and AWS Inferentia based instances in Amazon SageMaker JumpStart. The Meta Llama 3 models are a collection of pre-trained and fine-tuned generative text models. Amazon Elastic Compute Cloud (Amazon EC2) Trn1 and Inf2 instances, powered by AWS Trainium and AWS […]

As more powerful large language models (LLMs) are used to perform a variety of tasks with greater accuracy, the number of applications and services that are being built with generative artificial intelligence (AI) is also growing. With great power comes responsibility, and organizations want to make sure that these LLMs produce responses that align with […]

We are excited to announce the general availability of two advanced recipes in Amazon Personalize, User-Personalization-v2 and Personalized-Ranking-v2 (v2 recipes), which are built on the cutting-edge Transformers architecture to support larger item catalogs with lower latency. In this post, we summarize the new enhancements, and guide you through the process of training a model and providing recommendations for your users.

Embeddings are integral to various natural language processing (NLP) applications, and their quality is crucial for optimal performance. They are commonly used in knowledge bases to represent textual data as dense vectors, enabling efficient similarity search and retrieval. In Retrieval Augmented Generation (RAG), embeddings are used to retrieve relevant passages from a corpus to provide […]