AWS Machine Learning Blog

Category: Artificial Intelligence

New features for Amazon SageMaker Pipelines and the Amazon SageMaker SDK

Amazon SageMaker Pipelines allows data scientists and machine learning (ML) engineers to automate training workflows, which helps you create a repeatable process to orchestrate model development steps for rapid experimentation and model retraining. You can automate the entire model build workflow, including data preparation, feature engineering, model training, model tuning, and model validation, and catalog […]

Reduce the time taken to deploy your models to Amazon SageMaker for testing

Data scientists often train their models locally and look for a proper hosting service to deploy their models. Unfortunately, there’s no one set mechanism or guide to deploying pre-trained models to the cloud. In this post, we look at deploying trained models to Amazon SageMaker hosting to reduce your deployment time. SageMaker is a fully […]

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 […]

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 […]

Solution overview

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]