AWS Machine Learning Blog

Category: Advanced (300)

Recommend top trending items to your users using the new Amazon Personalize recipe

Amazon Personalize is excited to announce the new Trending-Now recipe to help you recommend items gaining popularity at the fastest pace among your users. Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users. It enables you to improve customer engagement by […]

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

This is joint post co-written by Leidos and AWS. Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. Leidos has partnered with AWS to develop an approach to privacy-preserving, confidential machine learning (ML) modeling where […]

Build a machine learning model to predict student performance using Amazon SageMaker Canvas

There has been a paradigm change in the mindshare of education customers who are now willing to explore new technologies and analytics. Universities and other higher learning institutions have collected massive amounts of data over the years, and now they are exploring options to use that data for deeper insights and better educational outcomes. You […]

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and […]

Remote monitoring of raw material supply chains for sustainability with Amazon SageMaker geospatial capabilities

Deforestation is a major concern in many tropical geographies where local rainforests are at severe risk of destruction. About 17% of the Amazon rainforest has been destroyed over the past 50 years, and some tropical ecosystems are approaching a tipping point beyond which recovery is unlikely. A key driver for deforestation is raw material extraction […]

Best practices for viewing and querying Amazon SageMaker service quota usage

Amazon SageMaker customers can view and manage their quota limits through Service Quotas. In addition, they can view near real-time utilization metrics and create Amazon CloudWatch metrics to view and programmatically query SageMaker quotas. SageMaker helps you build, train, and deploy machine learning (ML) models with ease. To learn more, refer to Getting started with […]

Build custom code libraries for your Amazon SageMaker Data Wrangler Flows using AWS Code Commit

As organizations grow in size and scale, the complexities of running workloads increase, and the need to develop and operationalize processes and workflows becomes critical. Therefore, organizations have adopted technology best practices, including microservice architecture, MLOps, DevOps, and more, to improve delivery time, reduce defects, and increase employee productivity. This post introduces a best practice […]

Few-click segmentation mask labeling in Amazon SageMaker Ground Truth Plus

Amazon SageMaker Ground Truth Plus is a managed data labeling service that makes it easy to label data for machine learning (ML) applications. One common use case is semantic segmentation, which is a computer vision ML technique that involves assigning class labels to individual pixels in an image. For example, in video frames captured by […]

Accelerate time to insight with Amazon SageMaker Data Wrangler and the power of Apache Hive

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler enables you to access data from a wide variety of popular sources (Amazon S3, Amazon Athena, Amazon Redshift, Amazon EMR and Snowflake) and over 40 other third-party sources. […]

Use a data-centric approach to minimize the amount of data required to train Amazon SageMaker models

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements. Applying these techniques allows ML practitioners […]