Take search to the next level with machine learning

Elastic is loaded with the latest advancements in machine learning and NLP. Easy to implement, flexible capabilities give you the tools to build semantic and image search, personalization, and question answering into your applications to measurably improve search experiences.

Dig deeper into Elastic's machine learning capabilities.

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Machine learning at your fingertips

With Elastic, you can build applications with natively run ML and vector search to achieve superior search relevance, performance, and personalization. Categorize datasets, detect anomalies, identify and anticipate trends.

  • Vector search

    Elastic is built with Lucene's vector fields and approximate nearest neighbor (ANN, using HNSW) search, matching search queries with vector-based search concepts that make search applications faster and more accurate — especially at scale.

  • NLP support & model management

    Support for modern natural language processing lets you use PyTorch and Python models with Elasticsearch ingest pipelines for sentiment analysis, text classification, and named entity recognition (NER). Import popular transformer models directly from Hugging Face.

  • Predictive models

    Build and apply predictive models (supervised learning) to classify data into categories or to forecast trends. To apply predictive models in Elastic, transformers map raw data into data frames.

Common ML Tasks

Add ML to your search use case

Applications of ML search are limitless, and Elastic's capabilities fuel hyper-relevant search that enhance search experiences and management behind the scenes.

  • Personalization

    Build search that tailors responses to end-user location, customer account or purchase history, or their role inside an organization with named entity recognition, text classification, and sentiment analysis.

  • Semantic search

    Natural language search lets users get back faster, more accurate results at scale using vector search and approximate nearest neighbor algorithms – which capture contextual information and unlock meaning behind search queries.

  • Image search

    Compare images across large data sets for product discovery and cross-sell, image tracking, and authentication.

  • Question-answering

    Get end-users relevant answers faster by finding similar questions in your website FAQ, help center, or support knowledge base using vector fields, text similarity search, fill-mask, and text classification tasks.

  • Content enrichment

    Organize content to show five-star customer reviews or similar news stories together on your website, categorize research data, or route customer support issues with named entity recognition, text embedding, zero-shot classification, and sentiment analysis.

  • Trend identification

    Spot and respond to patterns in your search data such as gaps in your content or product catalog using machine learning models that classify, cluster, and correlate search analytics and behavior.

Elastic Difference

Make the complex, simple

Elastic's tools bring integration, flexibility, and scalability to machine learning, so teams can easily use pretrained models and operate at scale. Tune or build your own models to meet domain-specific needs and innovate for your organization.

  • ML inside

    With ML in core Elasticsearch, integrate your data efficiently without needing to export it to an external endpoint.

  • Flexible applications

    Build modern ML-powered search with maximum configurability and ease of use.

  • Scalable for any use case

    Test, operationalize, and scale up Elasticsearch-style. ML is already included with your favorite search platform.