Category: Amazon Machine Learning

In this post, we build a secure enterprise application using AWS Amplify that invokes an Amazon SageMaker JumpStart foundation model, Amazon SageMaker endpoints, and Amazon OpenSearch Service to explain how to create text-to-text or text-to-image and Retrieval Augmented Generation (RAG). You can use this post as a reference to build secure enterprise applications in the Generative AI domain using AWS services.

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the business-as-usual operations is not an easy task. In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps). Furthermore, we deep dive on the most common generative AI use case of text-to-text applications and LLM operations (LLMOps), a subset of FMOps. The following figure illustrates the topics we discuss.

As part of the 2023 Data Science Conference (DSCO 23), AWS partnered with the Data Institute at the University of San Francisco (USF) to conduct a datathon. Participants, both high school and undergraduate students, competed on a data science project that focused on air quality and sustainability. The Data Institute at the USF aims to support cross-disciplinary research and education in the field of data science. The Data Institute and the Data Science Conference provide a distinctive fusion of cutting-edge academic research and the entrepreneurial culture of the technology industry in the San Francisco Bay Area.

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.

Data classification, extraction, and analysis can be challenging for organizations that deal with volumes of documents. Traditional document processing solutions are manual, expensive, error prone, and difficult to scale. AWS intelligent document processing (IDP), with AI services such as Amazon Textract, allows you to take advantage of industry-leading machine learning (ML) technology to quickly and […]

Computer vision (CV) is one of the most common applications of machine learning (ML) and deep learning. Use cases range from self-driving cars, content moderation on social media platforms, cancer detection, and automated defect detection. Amazon Rekognition is a fully managed service that can perform CV tasks like object detection, video segment detection, content moderation, […]

Breakthroughs in artificial intelligence (AI) and machine learning (ML) have been in the headlines for months—and for good reason. The emerging and evolving capabilities of this technology promises new business opportunities for customer across all sectors and industries. But the speed of this revolution has made it harder for organizations and consumers to assess what […]

Generative AI Foundations on AWS is a new technical deep dive course that gives you the conceptual fundamentals, practical advice, and hands-on guidance to pre-train, fine-tune, and deploy state-of-the-art foundation models on AWS and beyond. Developed by AWS generative AI worldwide foundations lead Emily Webber, this free hands-on course and the supporting GitHub source code […]

Fine-tuning large language models (LLMs) allows you to adjust open-source foundational models to achieve improved performance on your domain-specific tasks. In this post, we discuss the advantages of using Amazon SageMaker notebooks to fine-tune state-of-the-art open-source models. We utilize Hugging Face’s parameter-efficient fine-tuning (PEFT) library and quantization techniques through bitsandbytes to support interactive fine-tuning of […]

Amazon Personalize now enables popularity tuning for its Similar-Items recipe (aws-similar-items). Similar-Items generates recommendations that are similar to the item that a user selects, helping users discover new items in your catalog based on the previous behavior of all users and item metadata. Previously, this capability was only available for SIMS, the other Related_Items recipe […]