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There are different types of ML learning: Supervised Learning, Unsupervised Learning and Reinforcement Learning.

In Supervised Learning input is provided as a labeled dataset with the need for a supervisor, the model processes this data to provide the result and the output data patterns are known to the system.

Unsupervised learning is not supervised, it is self-organized learning. Its main aim is to explore the patterns and predict the output by finding an association between input values.

In Reinforcement Learning, the learning agent works as a reward system, the data is not predefined and the agent interacts with the environment, traveling from one state to another.

Supervised ML vs Unsupervised ML vs Reinforcement ML

Elastic uses Supervised Learning and Unsupervised Learning. The type of analysis that you choose depends on the questions or problems you want to address and the type of data you have available.

Elastic Anomaly Detection

Anomaly detection constructs a probability model and can run continuously to identify unusual events as they occur. (unsupervised)

Elastic Data Frame Analytics

Outlier detection identifies unusual points in a data set by analyzing how close each data point is to others and the density of the cluster of points around it. (unsupervised)

Classification predicts the class or category of a given data point in a dataset. (supervised)

Regression predicts continuous numerical values after it determines certain relationships among your data points. (supervised)

It is also possible to import a model to your Elasticsearch cluster even if the model is not trained by Elastic data frame analytics, following the appropriate JSON schema. Likewise, you can use third-party models to perform Natural Language Processing (NLP) tasks.

To use the Elastic Stack machine learning features, you must have the appropriate subscription level or the free trial period activated and at least one machine learning node in your cluster.

Our goal is to empower users with tools to get value and insights from their Elasticsearch data, and we view machine learning as a natural extension to the search and analytics capabilities in Elasticsearch, that’s why machine learning jobs are automatically distributed and managed across the Elasticsearch cluster in much the same way that indexes and shards are and one of the big focus areas is adding Machine Learning as integrated capability across all of our solutions.

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This post is part of a series that covers Artificial Intelligence with a focus on Elastic's (Creators of Elasticsearch) Machine Learning solution, aiming to introduce and exemplify the possibilities and options available, in addition to addressing the context and usability.