AWS Machine Learning Blog

Organizations are continuing to evaluate remote working arrangements and explore moving to a hybrid workforce model. Emerging trends suggest that not only has the number of online meetings attended by employees on a day-to-day basis increased, but also the number of attendees per meeting. One of the key challenges with online meetings is ensuring efficient […]

Whether you are developing a machine learning (ML) model for reducing operating cost, improving efficiency, or improving customer satisfaction, there are no perfect solutions when it comes to producing an effective model. From an ML development perspective, data scientists typically go through stages of data exploration, feature engineering, model development, and model training and tuning […]

Machine learning (ML) and deep learning (DL) are becoming effective tools for solving diverse computing problems, from image classification in medical diagnosis, conversational AI in chatbots, to recommender systems in ecommerce. However, ML models that have specific latency or high throughput requirements can become prohibitively expensive to run at scale on generic computing infrastructure. To […]

Eastern Australia is among the most fire-prone regions in the world. Although bushfires are a regular occurrence in Australia, the 2019–2020 bushfire crisis set ablaze over 17 million hectares of land (larger than the size of England), costing the Australian economy more than $100 billion between property, infrastructure, social, and environmental costs. With increasingly extreme […]

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, […]