Training Computer Vision Models with data in your Data Warehouse

Data Warehouse

Data warehouses are popular because they allow organizations to store large amounts of data from disparate sources in one place, making it easier to analyze and make decisions based on that data. Data warehouse software allows you to process, transform, and utilize data for decision-making. Data warehouses can provide a stable, centralized repository for large amounts of historical data. They can improve business processes and decision-making with actionable insights, and can increase a business's data strategy return on investment (ROI) and improve data quality.

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 data warehouses 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 their data warehouse. Kaspian offers native connectors for the most popular data warehouses. Just register your data warehouse as a 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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