AWS Machine Learning Blog

The fields of natural language processing (NLP), natural language understanding (NLU), and related branches of machine learning (ML) for text analysis have rapidly evolved to address use cases involving text classification, summarization, translation, and more. State-of-the art, general-purpose architectures such as transformers are making this evolution possible. Looking at text classification in particular, a supervised […]

Domestic infrastructure issues are a pain for everyone involved. Not only does it negatively affect customer satisfaction, it also has a cascading effect on businesses and their bottom line in terms of financials. Electric utility poles, for example, are an example of a cumbersome infrastructure issue to resolve. Normally, the standard wooden distribution pole is […]

AutoGluon-Tabular is an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning (ML) models on an unprocessed tabular dataset. In this post, we walk you through a way of using AutoGluon-Tabular as a code-free AWS Marketplace product. We use this process to train and deploy a highly […]

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. With Amazon Kendra, you can stop searching […]

After the article in Nature about the open-source of AlphaFold v2.0 on GitHub by DeepMind, many in the scientific and research community have wanted to try out DeepMind’s AlphaFold implementation firsthand. With compute resources through Amazon Elastic Compute Cloud (Amazon EC2) with Nvidia GPU, you can quickly get AlphaFold running and try it out yourself. […]

Many organizations spanning different sizes and industry verticals still rely on large volumes of documents to run their day-to-day operations. To solve this business challenge, customers are using intelligent document processing services from AWS such as Amazon Textract and Amazon Comprehend to help with extraction and process automation. Before you can extract text, key-value pairs, […]

If you have a corpus of internal documents that you frequently search through, Amazon Kendra can help you find your content faster and easier. These documents can be in different locations and repositories, and can be structured or unstructured. Amazon Kendra is a fully managed service backed by machine learning (ML). You don’t need to […]

Training your machine learning (ML) model and serving predictions is usually not the end of the ML project. The accuracy of ML models can deteriorate over time, a phenomenon known as model drift. Many factors can cause model drift, such as changes in model features. The accuracy of ML models can also be affected by […]

Today, we announced RStudio on Amazon SageMaker, the first machine learning (ML) integrated development environment (IDE) in the cloud for data scientists working in R. The open-source language R and its rich ecosystem with more than 18,000 packages has been a top choice for statisticians, quant analysts, data scientists, and ML engineers. RStudio on SageMaker […]

Today, we’re excited to announce RStudio on Amazon SageMaker, the industry’s first fully-managed RStudio integrated development environment (IDE) in the cloud. You can now bring the current RStudio licenses and migrate your self-managed RStudio environments to Amazon SageMaker in a few simple steps. RStudio is one of the most popular IDEs among R developers for […]