Training Computer Vision 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.

Computer Vision Models

Computer vision (CV) is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs and take actions or make recommendations based on that information. It trains convolutional neural networks to develop human-like vision capabilities for applications. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they see. Computer vision has many applications such as facial recognition, autonomous vehicles, medical imaging, robotics, surveillance, and more.
With the growing popularity of both Redshift for storage and computer vision models for AI deployments, it is unsurprising that many organizations are seeking to train computer vision 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 computer vision 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.

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