R Flashcards
range GET
A request that specifies a byte range of data to get for a download. If an object is large, you can break up a download into smaller units by sending multiple range GET requests that each specify a different byte range to GET.
raw email
A type of sendmail request with which you can specify the email headers and MIME types.
RDS
See Amazon Relational Database Service (Amazon RDS).
read replica
Amazon RDS: An active copy of another DB instance. Any updates to the data on the source DB instance are replicated to the read replica DB instance using the built-in replication feature of MySQL 5.1.
real-time predictions
Amazon Machine Learning: Synchronously generated predictions for individual data observations.
See Also batch prediction.
receipt handle
Amazon SQS: An identifier that you get when you receive a message from the queue. This identifier is required to delete a message from the queue or when changing a message’s visibility timeout.
receiver
The entity that consists of the network systems, software, and policies that manage email delivery for a recipient.
recipient
Amazon Simple Email Service (Amazon SES): The person or entity receiving an email message. For example, a person named in the “To” field of a message.
Redis
A fast, open source, in-memory key-value data structure store. Redis comes with a set of versatile in-memory data structures with which you can easily create a variety of custom applications.
reference
A means of inserting a property from one AWS resource into another. For example, you could insert an Amazon EC2 security group property into an Amazon RDS resource.
region
A named set of AWS resources in the same geographical area. A region comprises at least two Availability Zones.
regression model
Amazon Machine Learning: Preformatted instructions for common data transformations that fine-tune machine learning model performance.
regression model
A type of machine learning model that predicts a numeric value, such as the exact purchase price of a house.
regularization
A machine learning (ML) parameter that you can tune to obtain higher-quality ML models. Regularization helps prevent ML models from memorizing training data examples instead of learning how to generalize the patterns it sees (called overfitting). When training data is overfitted, the ML model performs well on the training data but does not perform well on the evaluation data or on new data.
replacement environment
The instances in a deployment group after the AWS CodeDeploy blue/green deployment.
replica shard
See shard.