Machine learning (ML) in Elasticsearch allows you to analyze and model data to uncover patterns, trends, and anomalies. This module will guide you through the basics of machine learning in Elasticsearch, including how to set up and use ML features, practical examples, and exercises to reinforce your understanding.

1. Anomaly Detection: Identifying unusual patterns in data that do not conform to expected behavior.
2. Datafeeds: Mechanisms to stream data from Elasticsearch indices to machine learning jobs.
3. Jobs: Configurations that define the type of analysis to be performed on the data.
4. Models: Trained machine learning algorithms that can make predictions or detect anomalies.

• Elasticsearch version 5.4 or later.
• Basic understanding of Elasticsearch indices and data structures.

Machine learning features are part of the X-Pack plugin in Elasticsearch. Ensure that X-Pack is installed and enabled.

1. Define the Job: Specify the type of analysis (e.g., anomaly detection) and the data to be analyzed.
2. Create a Datafeed: Stream data from an index to the job.
3. Start the Job: Begin the analysis process.

Step 1: Define the Job

Step 2: Create a Datafeed

Step 3: Start the Job

1. Objective: Detect anomalies in the average response time of a web application.
2. Steps:
   • Create an index web_logs with sample data.
   • Define a machine learning job to analyze the average response time.
   • Create a datafeed to stream data from web_logs to the job.
   • Start the job and observe the results.

Sample Data

Solution

1. Define the Job:

2. Create the Datafeed:

3. Start the Job:

1. Objective: Interpret the results of the anomaly detection job.
2. Steps:
   • Use the _ml/anomaly_detectors/response_time_anomaly/results endpoint to retrieve the results.
   • Identify any anomalies detected in the response time.

Solution

• Data Quality: Ensure that the data being analyzed is clean and well-structured.
• Bucket Span: Choose an appropriate bucket span for your analysis to balance between granularity and performance.
• Monitoring: Regularly monitor the performance and results of your machine learning jobs to ensure they are functioning as expected.

In this module, you learned how to set up and use machine learning features in Elasticsearch to detect anomalies in your data. You created a machine learning job, set up a datafeed, and started the job to analyze data. You also practiced creating and interpreting anomaly detection jobs through practical exercises. This knowledge prepares you to leverage machine learning in Elasticsearch for more advanced data analysis and insights.