Artificial Intelligence
Category: Technical How-to
Demystifying machine learning at the edge through real use cases
October 2023: Starting in April 26th, 2024, you can no longer access Amazon SageMaker Edge Manager. For more information about continuing to deploy your models to edge devices, see SageMaker Edge Manager end of life. Edge is a term that refers to a location, far from the cloud or a big data center, where you […]
Train machine learning models using Amazon Keyspaces as a data source
Many applications meant for industrial equipment maintenance, trade monitoring, fleet management, and route optimization are built using open-source Cassandra APIs and drivers to process data at high speeds and low latency. Managing Cassandra tables yourself can be time consuming and expensive. Amazon Keyspaces (for Apache Cassandra) lets you set up, secure, and scale Cassandra tables […]
Detect financial transaction fraud using a Graph Neural Network with Amazon SageMaker
Fraud plagues many online businesses and costs them billions of dollars each year. Financial fraud, counterfeit reviews, bot attacks, account takeovers, and spam are all examples of online fraud and malicious behaviors. Although many businesses take approaches to combat online fraud, these existing approaches can have severe limitations. First, many existing methods aren’t sophisticated or […]
Predict customer churn with no-code machine learning using Amazon SageMaker Canvas
Understanding customer behavior is top of mind for every business today. Gaining insights into why and how customers buy can help grow revenue. But losing customers (also called customer churn) is always a risk, and insights into why customers leave can be just as important for maintaining revenues and profits. Machine learning (ML) can help […]
Automate email responses using Amazon Comprehend custom classification and entity detection
In this post, we demonstrate how to create an automated email response solution using Amazon Comprehend. Organizations spend lots of resources, effort, and money on running their customer care operations to answer customer questions and provide solutions. Your customers may ask questions via various channels, such as email, chat, or phone, and deploying a workforce […]
Secure Amazon S3 access for isolated Amazon SageMaker notebook instances
In this post, we will demonstrate how to securely launch notebook instances in a private subnet of an Amazon Virtual Private Cloud (Amazon VPC), with internet access disabled, and to securely connect to Amazon Simple Storage Service (Amazon S3) using VPC endpoints. This post is for network and security architects that support decentralized data science […]
Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas
April 2023: This post was reviewed and updated with Amazon SageMaker Canvas’s new features and UI changes. Machine learning (ML) helps organizations increase revenue, drive business growth, and reduce cost by optimizing core business functions across multiple verticals, such as demand forecasting, credit scoring, pricing, predicting customer churn, identifying next best offers, predicting late shipments, […]
Control formality in machine translated text using Amazon Translate
Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation. Amazon Translate now supports formality customization. This feature allows you to customize the level of formality in your translation output. At the time of writing, the formality customization feature is available for six target languages: French, German, Hindi, Italian, […]
Automate a shared bikes and scooters classification model with Amazon SageMaker Autopilot
February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Amazon SageMaker Autopilot makes it possible for organizations to quickly build and deploy an end-to-end machine learning (ML) model and inference pipeline with just a few lines of code or even without […]
Process Amazon Redshift data and schedule a training pipeline with Amazon SageMaker Processing and Amazon SageMaker Pipelines
Customers in many different domains tend to work with multiple sources for their data: object-based storage like Amazon Simple Storage Service (Amazon S3), relational databases like Amazon Relational Database Service (Amazon RDS), or data warehouses like Amazon Redshift. Machine learning (ML) practitioners are often driven to work with objects and files instead of databases and […]






