Training Neural Network 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.

Neural Network Models

Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. They rely on training data to learn and improve their accuracy over time. Neural networks simulate how the brain learns by using multiple layers of nodes (input, hidden, and output) and they're able to learn both in supervised and unsupervised situations. They can recognize hidden patterns and correlations in raw data, cluster and classify it, and, over time, continuously learn and improve. Neural networks have many applications such as image recognition, speech recognition, natural language processing, autonomous vehicles, robotics, and more.

With the growing popularity of both data warehouses for storage and neural network models for AI deployments, it is unsurprising that many organizations are seeking to train neural network 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 neural network 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.