AWS Machine Learning

Effective October 1st, 2020, we’re reducing the prices for ml.p3 and ml.p2 instances in Amazon SageMaker by up to 18% so you can maximize your machine learning (ML) budgets and innovate with deep learning using t […]

NFL’s Next Gen Stats (NGS) powered by AWS accurately captures player and ball data in real time for every play and every NFL game—over 300 million data points per season—through the extensive use of sensors in pl […]

According to the 2018 National Highway Traffic Safety Administration (NHTSA) Traffic Safety Facts, in 2018, there were 857 fatal bicycle and motor vehicle crashes and an additional estimated 47,000 cycling […]

In this post, we show you how to deploy a TensorFlow based YOLOv4 model, using Keras optimized for inference on AWS Inferentia based Amazon EC2 Inf1 instances. You will set up a benchmarking environment to […]

Over the past few years, many automatic speech recognition (ASR) services have entered the market, offering a variety of different features. When deciding whether to use a service, you may want to evaluate its […]

AWS DeepComposer provides a creative and hands-on experience for learning generative AI and machine learning (ML). We recently launched the Edit melody feature, which allows you to add, remove, or edit specific […]

Starting Monday, October 5, 2020, the NVIDIA GPU Technology Conference (GTC) is offering online sessions for you to learn AWS best practices to accomplish your machine learning (ML), virtual workstations, high […]

We’re excited to announce the top 10 compositions and the winner of the AWS DeepComposer Chartbusters The Sounds of Science challenge. AWS DeepComposer provides a creative and hands-on experience for learning g […]

Amazon Personalize now makes it easier to manage your growing item and user catalogs with new APIs to incrementally add items and users in your datasets to create personalized recommendations. With the new […]

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! […]

In 2019, AWS announced Amazon SageMaker Studio, a unified integrated development environment (IDE) for machine learning (ML) development. You can write code, track experiments, visualize data, and perform […]

AWS DeepRacer allows you to get hands-on with machine learning (ML) through a fully autonomous 1/18th scale race car driven by reinforcement learning, a 3D racing simulator on the AWS DeepRacer console, a global […]

An enterprise typically houses multiple departments such as engineering, finance, legal, and marketing, creating a growing number of documents and content that employees need to access. Creating a search […]

As organizations grow larger in size, so does the need for having better document processing. In industries such as healthcare, legal, insurance, and banking, the continuous influx of paper-based or PDF documents […]

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 […]

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 needs thousands of labelled images and deep learning expertise. […]

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 […]

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 […]

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 […]

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 […]