Category: Artificial Intelligence

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 […]

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 […]

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 […]

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 […]

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 […]

This is a guest post co-written with Antony Vance from Intel. Customers are always looking for ways to improve the performance and response times of their machine learning (ML) inference workloads without increasing the cost per transaction and without sacrificing the accuracy of the results. Running ML workloads on Amazon SageMaker running Amazon Elastic Compute […]

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. For customers who have been developing ML models on premises, such as their local desktop, they want to migrate their legacy ML models to the AWS Cloud to fully take advantage of […]

This post is co-written with Mahima Agarwal, Machine Learning Engineer, and Deepak Mettem, Senior Engineering Manager, at VMware Carbon Black VMware Carbon Black is a renowned security solution offering protection against the full spectrum of modern cyberattacks. With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) […]

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 […]

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. […]