AWS Machine Learning Blog

Learn from the winner of the AWS DeepComposer Chartbusters challenge The Sounds of Science

AWS is excited to announce the winner of the AWS DeepComposer Chartbusters The Sounds of Science Challenge, Sungin Lee. AWS DeepComposer gives developers a creative way to get started with machine learning (ML). In June, we launched Chartbusters, a monthly global competition during which developers use AWS DeepComposer to create original compositions and compete to […]

Bringing your own custom container image to Amazon SageMaker Studio notebooks

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). SageMaker Studio lets data scientists spin up Studio notebooks to explore data, build models, launch Amazon SageMaker training jobs, and deploy hosted endpoints. Studio notebooks come with a set of pre-built images, which consist of the Amazon SageMaker Python SDK […]

Amazon Translate now enables you to mark content to not get translated

While performing machine translations, you may have situations where you wish to preserve specific sections of text from being translated, such as names, unique identifiers, or codes. We at the Amazon Translate team are excited to announce a tag modifications that allows you to specify what text should not be translated. This feature is available […]

Intelligently connect to customers using machine learning in the COVID-19 pandemic

The pandemic has changed how people interact, how we receive information, and how we get help. It has shifted much of what used to happen in-person to online. Many of our customers are using machine learning (ML) technology to facilitate that transition, from new remote cloud contact centers, to chatbots, to more personalized engagements online. […]

Announcing the AWS DeepComposer Chartbusters challenge, Keep Calm and Model On

We are back with another AWS DeepComposer Chartbusters challenge, Keep Calm and Model On! This challenge is open for submissions throughout AWS re:Invent until January 31, 2021. In this challenge, you can experiment with our newly launched Transformers algorithm and generate an original piece of music. Chartbusters is a global monthly challenge where you can […]

Adding custom data sources to Amazon Kendra

Amazon Kendra is a highly accurate and easy-to-use intelligent search service powered by machine learning (ML). Amazon Kendra provides native connectors for popular data sources like Amazon Simple Storage Service (Amazon S3), SharePoint, ServiceNow, OneDrive, Salesforce, and Confluence so you can easily add data from different content repositories and file systems into a centralized location. […]

Deploying reinforcement learning in production using Ray and Amazon SageMaker

Reinforcement learning (RL) is used to automate decision-making in a variety of domains, including games, autoscaling, finance, robotics, recommendations, and supply chain. Launched at AWS re:Invent 2018, Amazon SageMaker RL helps you quickly build, train, and deploy policies learned by RL. Ray is an open-source distributed execution framework that makes it easy to scale your […]

Explaining Amazon SageMaker Autopilot models with SHAP

Machine learning (ML) models have long been considered black boxes because predictions from these models are hard to interpret. However, recently, several frameworks aiming at explaining ML models were proposed. Model interpretation can be divided into local and global explanations. A local explanation considers a single sample and answers questions like “Why does the model […]

Creating an intelligent ticket routing solution using Slack, Amazon AppFlow, and Amazon Comprehend

Support tickets, customer feedback forms, user surveys, product feedback, and forum posts are some of the documents that businesses collect from their customers and employees. The applications used to collect these case documents typically include incident management systems, social media channels, customer forums, and email. Routing these cases quickly and accurately to support groups best […]

Real-time data labeling pipeline for ML workflows using Amazon SageMaker Ground Truth

High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it’s becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data. For example, you may want to create […]