Training Deep Learning Models with Elasticsearch Data

Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine that can be used to search all kinds of documents. It provides scalable search, has near real-time search, and supports multitenancy. Elasticsearch is popular because it's easy to use and has a lot of features. It's also open-source and has a large community of users. Elasticsearch is a NoSQL database because it doesn't use the traditional SQL relational database model. Instead, it uses a document-oriented model that allows for more flexibility and scalability.

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