AIOps Essentials (Autoscaling Kubernetes with Prometheus Metrics)

By John David Marx

Establish a baseline for AIOps by utilizing Prometheus for managing time-series metrics produced by Node Exporter and cAdvisor.

5 hours
  • 27 Lessons
  • 4 Hands-On Labs

About the course

This course establishes a baseline for AIOps by utilizing Prometheus for managing time-series metrics produced by Node Exporter and cAdvisor. The course guides the student through the fundamental concepts required for AIOps and the use of streaming metrics to influence autoscaling. The culmination of the course is the integration of the Prometheus rules with the Kubernetes APIServer to scale nodes in an active Kubernetes cluster.

Interactive Diagram: https://interactive.linuxacademy.com/diagrams/AIOpsEssentials.html

  • Chapter 1 6 Lessons Introduction 23:53

    An Important Note About A Cloud Guru and Linux Academy Courses

    1:19

    Introduction to Author

    0:36

    Introduction to This Course

    5:35

    Autoscaling a Cluster vs. Scaling an Infrastructure

    7:06

    The Case for AIOps

    6:12

    Machine Learning and Predictive Analytics

    3:05
  • Chapter 2 5 Lessons Monitoring and Metrics 8:07

    Prometheus

    1:56

    Prometheus Node Exporter

    1:21

    Google cAdvisor

    1:40

    Prometheus Node Exporter and cAdvisor Demo for Lab Prep

    3:10

    Using Prometheus with Kubernetes

    0:00 Hands-On Lab
  • Chapter 3 5 Lessons Exporting Metrics For AIOps 19:25

    Data Taxonomy

    7:21

    Relabeling With Prometheus

    4:47

    Aggregating Time Series Data

    5:13

    Using the Prometheus API

    2:04

    Using Python to Extract Prometheus Metrics

    0:00 Hands-On Lab
  • Chapter 4 3 Lessons Alerts and Triggers 11:13

    The Problem With Noise

    5:26

    Using Rules In Prometheus

    2:51

    Using Dashboards for Alerting

    2:56
  • Chapter 5 3 Lessons Using Linear Regression With Kubernetes 12:18

    Machine Learning Fundamentals

    8:51

    Using Python to Predict Scale

    3:27

    Using Python ML for Predictive Analytics

    0:00 Hands-On Lab
  • Chapter 6 3 Lessons Scaling an Infrastructure 12:24

    Scaling Nodes in a Kubernetes Cluster

    8:18

    Scaling a Hybrid Cloud With ML

    4:06

    Scaling a Cluster with Python

    0:00 Hands-On Lab
  • Chapter 7 2 Lessons Summation 3:03

    Conclusion and Next Steps

    1:43

    Credits and Resources

    1:20

What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

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  • Billed Annually
  • Renews in 12 months

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