AWS Machine Learning Blog

ONNX (Open Neural Network Exchange) is an open-source standard for representing deep learning models widely supported by many providers. ONNX provides tools for optimizing and quantizing models to reduce the memory and compute needed to run machine learning (ML) models. One of the biggest benefits of ONNX is that it provides a standardized format for […]

We are excited to announce the open-source release of GraphStorm 0.1, a low-code enterprise graph machine learning (ML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. With GraphStorm, you can build solutions that directly take into account the structure of relationships or interactions between billions […]

Data scientists need a consistent and reproducible environment for machine learning (ML) and data science workloads that enables managing dependencies and is secure. AWS Deep Learning Containers already provides pre-built Docker images for training and serving models in common frameworks such as TensorFlow, PyTorch, and MXNet. To improve this experience, we announced a public beta […]

Customers expect quick and efficient service from businesses in today’s fast-paced world. But providing excellent customer service can be significantly challenging when the volume of inquiries outpaces the human resources employed to address them. However, businesses can meet this challenge while providing personalized and efficient customer service with the advancements in generative artificial intelligence (generative […]

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 […]

Training large language models (LLMs) with billions of parameters can be challenging. In addition to designing the model architecture, researchers need to set up state-of-the-art training techniques for distributed training like mixed precision support, gradient accumulation, and checkpointing. With large models, the training setup is even more challenging because the available memory in a single […]

You can now retrain machine learning (ML) models and automate batch prediction workflows with updated datasets in Amazon SageMaker Canvas, thereby making it easier to constantly learn and improve the model performance and drive efficiency. An ML model’s effectiveness depends on the quality and relevance of the data it’s trained on. As time progresses, the […]

Amazon Lex is excited to announce Test Workbench, a new bot testing solution that provides tools to simplify and automate the bot testing process. During bot development, testing is the phase where developers check whether a bot meets the specific requirements, needs and expectations by identifying errors, defects, or bugs in the system before scaling. […]

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. Amazon Textract has a Tables feature within the AnalyzeDocument API that offers the ability to automatically extract tabular structures from any document. In this post, we discuss the improvements made to the Tables feature and […]

This blog post is co-written with Dr. Ebtesam Almazrouei, Executive Director–Acting Chief AI Researcher of the AI-Cross Center Unit and Project Lead for LLM Projects at TII. United Arab Emirate’s (UAE) Technology Innovation Institute (TII), the applied research pillar of Abu Dhabi’s Advanced Technology Research Council, has launched Falcon LLM, a foundational large language model […]