AWS Machine Learning Blog

Category: Artificial Intelligence

AWS Inferentia2 builds on AWS Inferentia1 by delivering 4x higher throughput and 10x lower latency

The size of the machine learning (ML) models––large language models (LLMs) and foundation models (FMs)––is growing fast year-over-year, and these models need faster and more powerful accelerators, especially for generative AI. AWS Inferentia2 was designed from the ground up to deliver higher performance while lowering the cost of LLMs and generative AI inference. In this […]

Deploy Falcon-40B with large model inference DLCs on Amazon SageMaker

Last week, Technology Innovation Institute (TII) launched TII Falcon LLM, an open-source foundational large language model (LLM). Trained on 1 trillion tokens with Amazon SageMaker, Falcon boasts top-notch performance (#1 on the Hugging Face leaderboard at time of writing) while being comparatively lightweight and less expensive to host than other LLMs such as llama-65B. In […]

Build custom chatbot applications using OpenChatkit models on Amazon SageMaker

Open-source large language models (LLMs) have become popular, allowing researchers, developers, and organizations to access these models to foster innovation and experimentation. This encourages collaboration from the open-source community to contribute to developments and improvement of LLMs. Open-source LLMs provide transparency to the model architecture, training process, and training data, which allows researchers to understand […]

Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

GPT-J is an open-source 6-billion-parameter model released by Eleuther AI. The model is trained on the Pile and can perform various tasks in language processing. It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis, and many more. GPT-J is a […]

Host ML models on Amazon SageMaker using Triton: ONNX Models

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 […]

Get started with the open-source Amazon SageMaker Distribution

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 […]

Exploring Generative AI in conversational experiences: An Introduction with Amazon Lex, Langchain, and SageMaker Jumpstart

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 […]

Introducing popularity tuning for Similar-Items in Amazon Personalize

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 […]

Retrain ML models and automate batch predictions in Amazon SageMaker Canvas using updated datasets

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 […]

Expedite the Amazon Lex chatbot development lifecycle with Test Workbench

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. […]