Define Anomaly Detection Machine Learning Jobs in Kibana 7.6

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Define Anomaly Detection Machine Learning Jobs in Kibana 7.6

Through the use of unsupervised statistical anomaly detection algorithms, Kibana manages to convert the mystery of machine learning (ML) into an easy-to-use and understandable interface from which machine learning jobs can be created and analyzed without a deep knowledge of how they work. In this hands-on lab, you will get to create and analyze the results of various machine learning jobs in Kibana to find hidden anomalies in the data.

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Lab Info

Level

Intermediate

Last updated

Sep 06, 2025

Duration

2h 30m

Challenge

Create and Run the flights ML Job
1. Create a single metric anomaly detection machine learning job for the flights index pattern.
2. Use the full flights data as the time range.
3. Configure the job to analyze the count of flights.
4. Configure the bucket span to be the Estimate bucket span output.
5. Set the job ID to "flights".
6. Create and configure the job to run in real time.
Challenge

Create and Run the flights-delayed ML Job
1. Create a multi metric anomaly detection machine learning job for the flights index pattern.
2. Use the full flights data as the time range.
3. Configure the job to analyze the high count of flights and the high sum of FlightDelayMin for each FlightDelayType.
4. Configure the bucket span to be the Estimate bucket span output.
5. Configure the job to ignore sparse data.
6. Set the job ID to "flights-delayed".
7. Create and configure the job to run in real time.
Challenge

Create and Run the flights-ticket-price ML Job
1. Create a new population anomaly detection machine learning job for the flights index pattern.
2. Use the full flights data as the time range.
3. Configure the job to analyze the average (mean) of AvgTicketPrice for the population of Carrier.
4. Configure the bucket span to be the Estimate bucket span output.
5. Set the job ID to "flights-ticket-price".
6. Create and configure the job to run in real time.

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