Elastic machine learning

Spot what you might otherwise miss.

Elastic machine learning — which is natively built into the Elastic Platform — helps teams get work done faster and build applications users crave. You can use Elastic machine learning to detect anomalies and rare events, surface correlations across metrics, analyze logs and traces, make predictions based on your data with classification and forecasting, and apply vector search and natural language processing.

Get actionable insights in minutes with Elasticsearch machine learning

Apply Elastic machine learning to your data to:

  • Natively integrate machine learning on a scalable and performant platform.
  • Apply unsupervised learning and preconfigured models that identify observability and security issues without having to worry about how to train an AI model.
  • Leverage actionable analytics that proactively surface threats and anomalies, accelerate problem resolution, identify customer behavioral trends, and improve your digital experiences.

To apply Elastic machine learning, you don’t need to have a data science team or design a system architecture. Our machine learning capabilities are natively integrated into the Elastic Platform, allowing you to quickly get started! There’s no need to move data to a 3rd party framework for model training.

Elastic machine learning gives you scalability and high-performance. You can use pre-configured models to identify system issues and detect security threats with our observability and security solutions. For search applications, tap into the power of vector search and modern natural language processing to bring your search-powered app to the next level.

For those use cases that require custom models and optimized performance, our tools let you adjust parameters and import optimized models from the PyTorch framework.

Elastic machine learning capabilities and their use cases

Accurate anomaly and outlier detection out-of-the-box

Unsupervised machine learning with Elastic helps you find patterns in your data. Use time series modeling to detect anomalies in single or multiple time series, population data, and forecast trends based on historical data. You can also detect anomalies in logs by grouping messages, and uncover root causes by reviewing anomaly influencers or fields correlated with deviations from baselines.

Screenshot of Machine Learning anomaly

Supervised machine learning with operational ease

To categorize your data and make predictions, train classification or regression models using data frame analytics in Elastic. Supervised models get you closer to the root cause of issues and can drive intelligent decisions in your applications.

You can use continuous index to convert application logs index into a user-centric activity view and build a fraud detection model using classification. Then you can apply your models to your incoming data at ingest, all without ever leaving Elastic. Learn more about applying supervised learning in Elastic here.

Vector search and modern natural language processing

Vector semantic search lets your users find what they mean, instead of being limited to keywords. They can search through textual data, images, and other unstructured data.

With Elastic machine learning, you can implement semantic search to make digital experiences more intuitive and results more relevant. Examples include:

  • Ecommerce product similarity search that displays relevant alternative products
  • Job recommendation and online dating — match based on profile compatibility, while restricting search by geolocation
  • Patent search — retrieve patents whose textual descriptions are similar

To get started, Elastic lets you import pre-trained BERT-like PyTorch models from hubs, like Huggingface.co, or the CLIP model from OpenAI. Learn how to implement image similarity in Elastic from this blog.


Elastic machine learning accelerates observability, security, and improves search

Get immediate value from machine learning with domain-specific use cases, built right into our observability, search and security solutions. DevOps engineers, SREs, and security analysts can get started right away without any prior experience with machine learning. Teams can automate anomaly detection and root cause analysis, reducing mean time to repair (MTTR). In addition, built-in capabilities such as NLP and vector search, take the search experience to the next level.

Use Elastic machine learning to:

  • Identify unusually slow response times directly from the APM service map
  • Discover unusual behavior and proactively address security threats
  • Customize anomaly detection for any type of data with easy-to-use wizard-based workflows
  • Enhance search experiences by enriching the ingested data with predictions

Automate alerts and identify root cause in observability

Accelerate problem detection and resolution with automated anomaly detection, correlations, and other AIOps capabilities built directly into our observability solution. With Elastic Observability, DevOps and SRE teams can identify unusually slow response times directly from the APM service map. Then apply machine learning without having to configure models.

Threat hunting powered by Machine learning

Machine learning powers threat detection in Elastic Security. You can reduce mean time to resolution (MTTR) by automatically identifying unusual activity in the SIEM app. Further assess the threat based on the influencers that triggered the alert. For threats that are difficult to identify, supervised models can disambiguate suspicious from benign activity, for example for living off the land attacks or domain generated algorithms.

Take search experiences to the next level

With Elastic, you can build search-powered applications using natively run vector search and natural language processing to achieve superior search relevance, performance, and personalization. Reference the models while configuring the ingestion pipeline, as shown on the right.