Training Computer Vision 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.

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 Elasticsearch 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 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 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.