Applying Machine Learning in StackOps

Overview

Deliver a practical introduction to StackOps’ machine learning features and explore how Elastic Machine Learning models process data in real time to detect anomalies. Learn the key benefits and prerequisites for ML, then apply them through guided, hands-on exercises. Use pre-configured anomaly detection jobs before progressing to custom ML configurations built for specific use cases. Strengthen observability practices through automated analysis and intelligent alerting in real-world environments.

Key Takeaways

• Understand the core benefits and concepts of Elastic Machine Learning
• Identify the pre-requisites for deploying ML jobs within the StackOps environment
• Utilise pre-configured ML jobs through the Anomaly Detection Wizard and Observability Overview
• Configure and run custom anomaly detection jobs to analyse specific time-series data
• Differentiate between various ML job types and apply them to relevant use cases

Who Should Attend

• Please refer to the job roles section.
• This course is designed for ICT&SS Professionals, Software Engineers, and Site Reliability Engineers (SREs) responsible for system health, monitoring, and data analysis. It is ideal if you are:
  • New to the machine learning features within StackOps.
  • Tasked with monitoring large volumes of data and need to automate the detection of unusual behaviour.
  • Looking to move from reactive to proactive monitoring by identifying anomalies before they become critical issues.
  • Interested in applying ML to your observability data but need a clear starting point.

Prerequisites

Prior knowledge of SRE concepts is a must. We recommend the Overview & Ingestion in StackOps and App Monitoring in StackOps to build a strong foundation in StackOps before attending this programme.