Author: Emily Webber

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 […]

In the pursuit of superior accuracy, deep learning models in areas such as natural language processing and computer vision have significantly grown in size in the past few years, frequently counted in tens to hundreds of billions of parameters. Training these gigantic models is challenging and requires complex distribution strategies. Data scientists and machine learning […]

So much data, so little time. Machine learning (ML) experts, data scientists, engineers and enthusiasts have encountered this problem the world over. From natural language processing to computer vision, tabular to time series, and everything in-between, the age-old problem of optimizing for speed when running data against as many GPUs as you can get has […]

November 2023: This post was reviewed for accuracy. The last few years have seen rapid development in the field of natural language processing (NLP). While hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly run into issues scaling their large language models […]

May 2023: The functionality described in this blog post, is now natively available in SageMaker Studio, and can be installed as an extension into any Jupyter environment. For more information refer to: Schedule your notebooks from any JupyterLab environment using the Amazon SageMaker JupyterLab extension Operationalize your Amazon SageMaker Studio notebooks as scheduled notebook jobs […]