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Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers with intermediate Python and basic machine learning skills

Who should attend & recommended skills

  • This course is designed for developers who want to leverage Elastic Stacks machine learning features to gain valuable insight from your data.
  • Skill-level: Foundation-level Machine Learning with the Elastic Stack skills for Intermediate skilled team members. This is not a basic class.
  • Machine Learning: Basic (1-2 years’ experience)
  • Python: Intermediate (3-5 years’ experience)

About this course

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The course starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the lessons, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding lessons, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this course, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.

Skills acquired & topics covered

  • Combining machine learning with the analytic capabilities of Elastic Stack
  • Analyzing large volumes of search data and gain actionable insight from them
  • Using external analytical tools with your Elastic Stack to improve its performance
  • Installing the Elastic Stack to use machine learning features
  • Understanding how Elastic machine learning is used to detect a variety of anomaly types
  • Applying effective anomaly detection to IT operations and security analytics
  • Leveraging the output of Elastic machine learning in custom views, dashboards, and proactive alerting
  • Combining your created jobs to correlate anomalies of different layers of infrastructure
  • Learning various tips and tricks to get the most out of Elastic machine learning

Course breakdown / modules

  • Overcoming the historical challenges
  • Theory of operation
  • Operationalization
  • Supporting indices
  • The orchestration

  • Installing the Elastic Stack
  • A guided tour of Elastic ML features

  • How to understand the normal rate of occurrence
  • Exploring count functions
  • Counting in population analysis
  • Detecting things that rarely occur
  • Counting message-based logs via categorization

  • Holistic application visibility
  • Data organization
  • Bringing it all together for root cause analysis

  • Security in the field
  • Threat hunting architecture
  • Investigation analytics

  • Results presentation
  • The results index
  • Alerts from the Machine Learning UI in Kibana
  • Creating ML alerts manually

  • Visualization options in Kibana
  • Preparing data for anomaly detection analysis
  • Building the visualizations

  • Introduction to Canvas
  • Building Elastic ML Canvas slides

  • Forecasting versus prophesying
  • Forecasting use cases
  • Forecasting – theory of operation
  • Single time series forecasting
  • Forecast results
  • Multiple time series forecasting

  • Job groups
  • Influencers in split versus non-split jobs
  • Using ML on scripted fields
  • Using one-sided ML functions to your advantage
  • Ignoring time periods
  • Don’t over-engineer the use case
  • ML job throughput considerations
  • Top-down alerting by leveraging custom rules
  • Sizing ML deployments