AWS Machine Learning Blog

Customers need machine learning (ML) models to detect objects that are interesting for their business. In most cases doing so is hard as these models need thousands of labeled images and deep learning expertise.  Generating this data can take months to gather, and can require large teams of labelers to prepare it for use. In […]

Amazon Transcribe is a fully-managed automatic speech recognition service (ASR) that makes it easy to add speech-to-text capabilities to voice-enabled applications. As our service grows, so does the diversity of our customer base, which now spans domains such as insurance, finance, law, real estate, media, hospitality, and more. Naturally, customers in different market segments have […]

AWS has expanded the availability of Amazon EC2 Inf1 instances to four new AWS Regions, bringing the total number of supported Regions to 11: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, Paris), and South America (São Paulo). Amazon EC2 Inf1 instances are powered by AWS […]

A typical machine learning (ML) workflow involves processes such as data extraction, data preprocessing, feature engineering, model training and evaluation, and model deployment. As data changes over time, when you deploy models to production, you want your model to learn continually from the stream of data. This means supporting the model’s ability to autonomously learn […]

Bidirectional Encoder Representations from Transformers (BERT) [1] has become one of the most popular models for natural language processing (NLP) applications. BERT can outperform other models in several NLP tasks, including question answering and sentence classification. Training the BERT model on large datasets is expensive and time consuming, and achieving low latency when performing inference […]

Many organizations use Amazon SageMaker for their machine learning (ML) requirements and source data from a data lake stored on Amazon Simple Storage Service (Amazon S3). The petabyte scale source data on Amazon S3 may not always be clean because data lakes ingest data from several source systems, such as like flat files, external feeds, […]

With the 2020 football season kicking off, Amazon Web Services (AWS) is continuing its work with the National Football League (NFL) on several ongoing game-changing initiatives. Specifically, the NFL and AWS are teaming up to develop state-of-the-art cloud technology using machine learning (ML) aimed at aiding the officiating process through real-time football detection. As a […]

Optical character recognition (OCR) technology, which enables extracting text from an image, has been around since the mid-20th century, and continues to be a research topic today. OCR and document understanding are still vibrant areas of research because they’re both valuable and hard problems to solve. AWS has been investing in improving OCR and document […]

In recent years, businesses are increasingly looking for ways to integrate the power of machine learning (ML) into business decision-making. This post demonstrates the use case of creating an optimal incentive program to offer customers identified as being at risk of leaving for a competitor, or churning. It extends a popular ML use case, predicting […]

In this post, we show you how to select the right metadata for your use case when building a recommendation engine using Amazon Personalize. The aim is to help you optimize your models to generate more user-relevant recommendations. We look at which metadata is most relevant to include for different use cases, and where you […]