On the other hand, there are some serious open-source photo editing programs that can replace commercial programs for many people. And indeed, these are by far the most popular. Future serverless architectures will take application simplicity to a whole new level.When it comes to image editing, most people probably think of commercial applications like Adobe Lightroom, Photoshop, or Phase One’s Capture One Pro. You can experience simplified performance monitoring, a smaller maintenance footprint, and fewer security vulnerabilities - to name a few. Reducing the number of services needed by an application has benefits beyond scaling. Elastic Cloud allows you to easily scale up and down, depending on your current search workload. Models live alongside nodes running search in the same cluster, which applies to on-premise clusters, and even more so if you deploy to the cloud. This means you need to hire experts, add development time to your project, and set aside resources to manage it over time. In other popular frameworks, applying deep neural networks and NLP models occurs separately from scaling searches on large data sets. ![]() Vector search and NLP inference endpoints are integrated within a scalable search platform. With Elastic you don’t need separate services for running kNN search and vectorizing your search input. Implementing image similarity search in Elastic provides you with istinct advantages. We’ll get started by stepping back a little and explaining how both similarity and semantic search are powered by vector search. If you’re actually more interested in semantic search on text rather than images, review the multi-blog series on natural language processing (NLP) to learn about text embeddings and vector search, named entity recognition (NER), sentiment analysis, and how to apply these techniques in Elastic. In this overview blog, you’ll go behind the scenes to better understand the architecture required to apply vector search to image data with Elastic.
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