Introducing self-service quota management and higher default service quotas for Amazon Textract

Today, we’re excited to announce self-service quota management support for Amazon Textract via the AWS Service Quotas console, and higher default service quotas in select AWS Regions. Customers tell us they need quick turnaround times to process their requests for quota increases and visibility into their service quotas so they may continue to scale their … Read more

Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

This post is co-written by Goktug Cinar, Michael Binder, and Adrian Horvath from Bosch Center for Artificial Intelligence (BCAI). Revenue forecasting is a challenging yet crucial task for strategic business decisions and fiscal planning in most organizations. Often, revenue forecasting is manually performed by financial analysts and is both time consuming and subjective. Such manual … Read more

Detect population variance of endangered species using Amazon Rekognition

Our planet faces a global extinction crisis. UN Report shows a staggering number of more than a million species feared to be on the path of extinction. The most common reasons for extinction include loss of habitat, poaching, and invasive species. Several wildlife conservation foundations, research scientists, volunteers, and anti-poaching rangers have been working tirelessly … Read more

How Amazon Search reduced ML inference costs by 85% with AWS Inferentia

Amazon’s product search engine indexes billions of products, serves hundreds of millions of customers worldwide, and is one of the most heavily used services in the world. The Amazon Search team develops machine learning (ML) technology that powers the Amazon.com search engine and helps customers search effortlessly. To deliver a great customer experience and operate … Read more

Amazon Comprehend Targeted Sentiment adds synchronous support

Earlier this year, Amazon Comprehend, a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text, launched the Targeted Sentiment feature. With Targeted Sentiment, you can identify groups of mentions (co-reference groups) corresponding to a single real-world entity or attribute, provide the sentiment associated with each entity mention, and offer … Read more

Run machine learning enablement events at scale using AWS DeepRacer multi-user account mode

This post was co-written by Marius Cealera, Senior Partner Solutions Architect at AWS, Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, Cloud Architect at Accenture. Machine learning (ML) is a high-stakes business priority, with companies spending $306 billion on ML applications in the past 3 years. According to Accenture, companies that scale ML across … Read more

Enable intelligent decision-making with Amazon SageMaker Canvas and Amazon QuickSight

Every company, regardless of its size, wants to deliver the best products and services to its customers. To achieve this, companies want to understand industry trends and customer behavior, and optimize internal processes and data analyses on a routine basis. This is a crucial component of a company’s success. A very prominent part of the … Read more

Amazon SageMaker Autopilot is up to eight times faster with new ensemble training mode powered by AutoGluon

Amazon SageMaker Autopilot has added a new training mode that supports model ensembling powered by AutoGluon. Ensemble training mode in Autopilot trains several base models and combines their predictions using model stacking. For datasets less than 100 MB, ensemble training mode builds machine learning (ML) models with high accuracy quickly—up to eight times faster than … Read more

Configure a custom Amazon S3 query output location and data retention policy for Amazon Athena data sources in Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler reduces the time that it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of … Read more

Use RStudio on Amazon SageMaker to create regulatory submissions for the life sciences industry

Pharmaceutical companies seeking approval from regulatory agencies such as the US Food & Drug Administration (FDA) or Japanese Pharmaceuticals and Medical Devices Agency (PMDA) to sell their drugs on the market must submit evidence to prove that their drug is safe and effective for its intended use. A team of physicians, statisticians, chemists, pharmacologists, and … Read more

Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. These algorithms and models can be used for both supervised and unsupervised learning. They can process various types of input data, including tabular, … Read more

Parallel data processing with RStudio on Amazon SageMaker

Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE, and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) … Read more

Discover insights from Zendesk with Amazon Kendra intelligent search

Customer relationship management (CRM) is a critical tool that organizations maintain to manage customer interactions and build business relationships. Zendesk is a CRM tool that makes it easy for customers and businesses to keep in sync. Zendesk captures a wealth of customer data, such as support tickets created and updated by customers and service agents, … Read more

Amazon SageMaker Automatic Model Tuning now provides up to three times faster hyperparameter tuning with Hyperband

Amazon SageMaker Automatic Model Tuning introduces Hyperband, a multi-fidelity technique to tune hyperparameters as a faster and more efficient way to find an optimal model. In this post, we show how automatic model tuning with Hyperband can provide faster hyperparameter tuning—up to three times as fast. The benefits of Hyperband Hyperband presents two advantages over … Read more

Use Amazon SageMaker Data Wrangler for data preparation and Studio Labs to learn and experiment with ML

Amazon SageMaker Studio Lab is a free machine learning (ML) development environment based on open-source JupyterLab for anyone to learn and experiment with ML using AWS ML compute resources. It’s based on the same architecture and user interface as Amazon SageMaker Studio, but with a subset of Studio capabilities. When you begin working on ML … Read more

Announcing Visual Conversation Builder for Amazon Lex

Amazon Lex is a service for building conversational interfaces using voice and text. Amazon Lex provides high-quality speech recognition and language understanding capabilities. With Amazon Lex, you can add sophisticated, natural language bots to new and existing applications. Amazon Lex reduces multi-platform development efforts, allowing you to easily publish your speech or text chatbots to … Read more

Get better insight from reviews using Amazon Comprehend

“85% of buyers trust online reviews as much as a personal recommendation” – Gartner Consumers are increasingly engaging with businesses through digital surfaces and multiple touchpoints. Statistics show that the majority of shoppers use reviews to determine what products to buy and which services to use. As per Spiegel Research Centre, the purchase likelihood for … Read more

Prepare data at scale in Amazon SageMaker Studio using serverless AWS Glue interactive sessions

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and … Read more

How Medidata used Amazon SageMaker asynchronous inference to accelerate ML inference predictions up to 30 times faster

This post is co-written with Rajnish Jain, Priyanka Kulkarni and Daniel Johnson from Medidata. Medidata is leading the digital transformation of life sciences, creating hope for millions of patients. Medidata helps generate the evidence and insights to help pharmaceutical, biotech, medical devices, and diagnostics companies as well as academic researchers with accelerating value, minimizing risk, … Read more

Deploy large models on Amazon SageMaker using DJLServing and DeepSpeed model parallel inference

The last few years have seen rapid development in the field of natural language processing (NLP). Although hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly encounter issues deploying their large language models. Today, we announce new capabilities in Amazon SageMaker that … Read more

Tips to improve your Amazon Rekognition Custom Labels model

In this post, we discuss best practices to improve the performance of your computer vision models using Amazon Rekognition Custom Labels. Rekognition Custom Labels is a fully managed service to build custom computer vision models for image classification and object detection use cases. Rekognition Custom Labels builds off of the pre-trained models in Amazon Rekognition, which … Read more

Use ADFS OIDC as the IdP for an Amazon SageMaker Ground Truth private workforce

To train a machine learning (ML) model, you need a large, high-quality, labeled dataset. Amazon SageMaker Ground Truth helps you build high-quality training datasets for your ML models. With Ground Truth, you can use workers from either Amazon Mechanical Turk, a vendor company of your choosing, or an internal, private workforce to enable you to … Read more

How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

Amp is a new live radio app from Amazon. With Amp, you can host your own radio show and play songs from the Amazon Music catalog, or tune in and listen to shows other Amp users are hosting. In an environment where content is plentiful and diverse, it’s important to tailor the user experience to … Read more

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

Amp, the new live radio app from Amazon, is a reinvention of radio featuring human-curated live audio shows. It’s designed to provide a seamless customer experience to listeners and creators by debuting interactive live audio shows from your favorite artists, radio DJs, podcasters, and friends. However, as a new product in a new space for … Read more

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

This is a guest blog post cowritten with athenahealth. athenahealth a leading provider of network-enabled software and services for medical groups and health systems nationwide. Its electronic health records, revenue cycle management, and patient engagement tools allow anytime, anywhere access, driving better financial outcomes for its customers and enabling its provider customers to deliver better quality … Read more

Transfer learning for TensorFlow image classification models in Amazon SageMaker

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, … Read more

Improve transcription accuracy of customer-agent calls with custom vocabulary in Amazon Transcribe

Many AWS customers have been successfully using Amazon Transcribe to accurately, efficiently, and automatically convert their customer audio conversations to text, and extract actionable insights from them. These insights can help you continuously enhance the processes and products that directly improve the quality and experience for your customers. In many countries, such as India, English … Read more

Detect audio events with Amazon Rekognition

When most people think of using machine learning (ML) with audio data, the use case that usually comes to mind is transcription, also known as speech-to-text. However, there are other useful applications, including using ML to detect sounds. Using software to detect a sound is called audio event detection, and it has a number of … Read more

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