Training Neural Network 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.

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 Elasticsearch 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 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 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.