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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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Path Info

Level
Clock icon Intermediate
Duration
Clock icon 2h 30m
Published
Clock icon Apr 23, 2021

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Table of Contents

  1. 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.

  2. 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.

  3. 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.
A Cloud Guru - Labs - Define Anomaly Detection Machine Learning Jobs in Kibana 7.6
Author
A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

What's a lab?

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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We will provide the credentials and environment necessary for you to practice right within your browser.

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Follow along with the author’s guided walkthrough and build something new in your provided environment!

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