Training Deep Learning Models with data in your NoSQL Database

NoSQL Database

NoSQL databases are popular because they are highly scalable and flexible, making them ideal for handling large amounts of unstructured data. They can also be used to store structured data. NoSQL databases are designed to handle big data and can be used to store data in a variety of formats including JSON, XML, and BSON. They are also highly available and fault-tolerant. Additionally, they are designed to scale horizontally, allowing companies to add more computing power and storage as needed.

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 NoSQL databases 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 their NoSQL database. Kaspian offers native connectors for the most popular NoSQL databases. Just register your NoSQL database as a 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.