AWS Machine Learning Blog

Category: Artificial Intelligence

Index your web crawled content using the new Web Crawler for Amazon Kendra

In this post, we show how to index information stored in websites and use the intelligent search in Amazon Kendra to search for answers from content stored in internal and external websites. In addition, the ML-powered intelligent search can accurately get answers for your questions from unstructured documents with natural language narrative content, for which keyword search is not very effective.

New – No-code generative AI capabilities now available in Amazon SageMaker Canvas

Launched in 2021, Amazon SageMaker Canvas is a visual, point-and-click service that allows business analysts and citizen data scientists to use ready-to-use machine learning (ML) models and build custom ML models to generate accurate predictions without the need to write any code. Ready-to-use models enable you to derive immediate insights from text, image, and document […]

Whisper models for automatic speech recognition now available in Amazon SageMaker JumpStart

Today, we’re excited to announce that the OpenAI Whisper foundation model is available for customers using Amazon SageMaker JumpStart. Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680 thousand hours of labelled data, Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need […]

Mistral 7B foundation models from Mistral AI are now available in Amazon SageMaker JumpStart

Today, we are excited to announce that the Mistral 7B foundation models, developed by Mistral AI, are available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. With 7 billion parameters, Mistral 7B can be easily customized and quickly deployed. You can try out this model with SageMaker JumpStart, a […]

Use no-code machine learning to derive insights from product reviews using Amazon SageMaker Canvas sentiment analysis and text analysis models

According to Gartner, 85% of software buyers trust online reviews as much as personal recommendations. Customers provide feedback and reviews about products they have purchased through many channels, including review websites, vendor websites, sales calls, social media, and many others. The problem with the increasing volume of customer reviews across multiple channels is that it […]

Prepare your data for Amazon Personalize with Amazon SageMaker Data Wrangler

A recommendation engine is only as good as the data used to prepare it. Transforming raw data into a format that is suitable for a model is key to getting better personalized recommendations for end-users. In this post, we walk through how to prepare and import the MovieLens dataset, a dataset prepared by GroupLens research […]

Personalize your generative AI applications with Amazon SageMaker Feature Store

In this post, we elucidate the simple yet powerful idea of combining user profiles and item attributes to generate personalized content recommendations using LLMs. As demonstrated throughout the post, these models hold immense potential in generating high-quality, context-aware input text, which leads to enhanced recommendations. To illustrate this, we guide you through the process of integrating a feature store (representing user profiles) with an LLM to generate these personalized recommendations.

Build an image-to-text generative AI application using multimodality models on Amazon SageMaker

In this post, we provide an overview of popular multimodality models. We also demonstrate how to deploy these pre-trained models on Amazon SageMaker. Furthermore, we discuss the diverse applications of these models, focusing particularly on several real-world scenarios, such as zero-shot tag and attribution generation for ecommerce and automatic prompt generation from images.

Improve prediction quality in custom classification models with Amazon Comprehend

In this post, we explain how to build and optimize a custom classification model using Amazon Comprehend. We demonstrate this using an Amazon Comprehend custom classification to build a multi-label custom classification model, and provide guidelines on how to prepare the training dataset and tune the model to meet performance metrics such as accuracy, precision, recall, and F1 score.

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

Large language models (LLMs) have captured the imagination and attention of developers, scientists, technologists, entrepreneurs, and executives across several industries. These models can be used for question answering, summarization, translation, and more in applications such as conversational agents for customer support, content creation for marketing, and coding assistants. Recently, Meta released Llama 2 for both […]