AWS Machine Learning Blog

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 […]

Automate Amazon Rekognition Custom Labels model training and deployment using AWS Step Functions

With Amazon Rekognition Custom Labels, you can have Amazon Rekognition train a custom model for object detection or image classification specific to your business needs. For example, Rekognition Custom Labels can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected […]

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 […]

Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

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 […]

Intelligently search your organization’s Microsoft Teams data source with the Amazon Kendra connector for Microsoft Teams

Organizations use messaging platforms like Microsoft Teams to bring the right people together to securely communicate with each other and collaborate to get work done. Microsoft Teams captures invaluable organizational knowledge in the form of the information that flows through it as users collaborate. However, making this knowledge easily and securely available to users can […]

Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

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 […]