AWS Machine Learning Blog

This is a guest blog post by Bryan Pelley, COO of Protagonist. Protagonist, in their own words “helps organizations communicate more effectively through a data-driven understanding of public discourse.” Protagonist is a pioneer of the art and science of understanding narratives. We define narratives as the beliefs that an audience holds that are  composed of […]

Amazon SageMaker gives you the flexibility that you need to address sophisticated business problems with your machine learning workloads. Built-in algorithms help you get started quickly.  In this blog post we’ll outline how you can extend the built-in factorization machines algorithm to predict top x recommendations. This approach is ideal when you want to generate […]

Amazon Comprehend customers are solving a variety of use cases with custom classification and entity type models. For example, customers are building classifiers to organize their daily customer feedback into categories like “loyalty,” “sales,” or “product defect.” Custom entity models enable customers to analyze text for their own terms and phrases, such as product IDs […]

We are excited to introduce two highly requested features to automatic model tuning in Amazon SageMaker: random search and hyperparameter scaling. This post describes these features, explains when and how to enable them, and shows how they can improve your search for hyperparameters that perform well. If you are in a hurry, you’ll be happy […]

Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics for important workloads. For example, analyzing market research reports for key market indicators or data that contains PII information. Customers that work with highly sensitive, encrypted data can now easily enable Comprehend to work with this encrypted data via an integration […]

From developer to machine learning developer The AWS DeepRacer League is the world’s first autonomous racing league open to developers of all skill levels and it kicked off last week in Santa Clara, California. Chris Miller was crowned our first champion of the 2019 season. Chris is the founder of Cloud Brigade, based in Santa Cruz, […]

Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for machine learning (ML). You can use your own workers, a choice of vendor-managed workforces that specialize in data labeling, or a public workforce powered by Amazon Mechanical Turk to provide the human-generated labels. To get high-quality labels, you must provide simple, concise, […]

One of the problems that business owners commonly face is detecting when something unusual is happening in their business. Detecting unusual user activity or changes in daily traffic patterns are just some of the challenges. With an ever-increasing amount of data and metrics, detecting anomalies with the help of machine learning is a great way […]

The Amazon Elastic Inference (EI) setup tool is a Python script that enables you to quickly get started with EI. Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75 percent. If you are using EI for the […]

Today, at the AWS Summit in Santa Clara, California, we kicked off the 2019 season of the world’s first global autonomous racing league. The AWS DeepRacer League allows developers of all skill levels to get hands on with machine learning through a series of live racing events at AWS Global Summits around the world. The […]