Training Deep Learning Models with Redshift Data

Redshift

Amazon Redshift is a popular data warehousing solution that can handle data on an exabytes scale. It is useful for processing real-time analytics, combining multiple data sources, log analysis, or more. Redshift uses parallel-processing and compression to decrease command execution time. This allows Redshift to perform operations on billions of rows at once. This also makes Redshift useful for storing and analyzing large quantities of data from logs or live feeds through a source such as Amazon Kinesis Data Firehose.

Deep Learning Models

Deep learning (DL) is a subset of machine learning that uses neural networks with three or more layers to simulate the behavior of the human brain. Deep learning models are popular because they can learn from large amounts of data and perform tasks that would normally require human intelligence to complete. Deep learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), restricted Boltzmann machines (RBMs), autoencoders, generative adversarial networks (GANs), residual neural networks (ResNets), self-organizing maps (SOMs), deep belief networks (DBNs), and multilayer perceptrons (MLPs).

With the growing popularity of both Redshift for storage and deep learning models for AI deployments, it is unsurprising that many organizations are seeking to train deep learning models using data in Redshift. Kaspian offers native connectors for this operation. Just register your Redshift datastore and link your model training job; Kaspian's autoscaling compute layer makes it easy to train and deploy deep learning models using any data in your cloud with minimal setup or management.

Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.