Training Computer Vision Models with Snowflake Data

Snowflake

Snowflake is a cloud-based data warehousing company that provides a platform for data storage, processing and analysis. It is considered cloud-agnostic, as it operates across Amazon Web Services (AWS), Microsoft Azure or Google Cloud. Snowflake delivers a platform that is fast, flexible, and user-friendly. It provides the means for not only data storage but also processing and analysis. One of the main reasons that Snowflake is gaining recognition as the top cloud data warehousing solution is thanks to its architecture, which consists of dynamic, scalable computing power with usage-based charges out-of-the-box features such as data cloning and sharing, on-the-fly scalable computing and third-party tool support.

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 Snowflake 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 Snowflake. Kaspian offers native connectors for this operation. Just register your Snowflake 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.