AWS Machine Learning Blog

Category: Artificial Intelligence

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 […]

Read More

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. […]

Read More

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 […]

Read More

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. […]

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

Deploying and using the Document Understanding Solution

Based on our day to day experience, the information we consume is entirely digital. We read the news on our mobile devices far more than we do from printed copy newspapers. Tickets for sporting events, music concerts, and airline travel are stored in apps on our phones. One could go weeks or longer without needing […]

Read More

Training and serving H2O models using Amazon SageMaker

Model training and serving steps are two essential pieces of a successful end-to-end machine learning (ML) pipeline. These two steps often require different software and hardware setups to provide the best mix for a production environment. Model training is optimized for a low-cost, feasible total run duration, scientific flexibility, and model interpretability objectives, whereas model […]

Read More