AWS Machine Learning Blog

We are back with a spooktacular AWS DeepComposer Chartbusters challenge, Track or Treat! In this challenge, you can interactively collaborate with the ghost in the machine (learning) and compose spooky music! Chartbusters is a global monthly challenge where you can use AWS DeepComposer to create original compositions on the console using machine learning techniques, compete […]

July 2023: This post was reviewed for accuracy. Apache Spark is a unified analytics engine for large scale, distributed data processing. Typically, businesses with Spark-based workloads on AWS use their own stack built on top of Amazon Elastic Compute Cloud (Amazon EC2), or Amazon EMR to run and scale Apache Spark, Hive, Presto, and other […]

Every day there is something new going on in the world of AWS Machine Learning—from launches to new use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each month. Check back at the end of each month for the latest roundup. Launches This […]

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 […]