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Working in a hands-on learning environment, led by our Machine Learning with the Elastic Stack expert instructor, students will learn about and explore: Combine machine learning with the analytic capabilities of Elastic Stack.Analyze large volumes of search data and gain actionable insight from them.Use external analytical tools with your Elastic Stack to improve its performance.

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Last Updated

January 9, 2021

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Course Description

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.

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Course Curriculum

    • Machine Learning for IT 00:00:00
    • Overcoming the historical challenges 00:00:00
    • Theory of operation 00:00:00
    • Operationalization 00:00:00
    • Supporting indices 00:00:00
    • The orchestration 00:00:00
    • Installing the Elastic Stack with Machine Learning 00:00:00
    • Installing the Elastic Stack 00:00:00
    • A guided tour of Elastic ML features 00:00:00
    • Event Change Detection 00:00:00
    • How to understand the normal rate of occurrence 00:00:00
    • Exploring count functions 00:00:00
    • Counting in population analysis 00:00:00
    • Detecting things that rarely occur 00:00:00
    • Counting message-based logs via categorization 00:00:00
    • IT Operational Analytics and Root Cause Analysis 00:00:00
    • Holistic application visibility 00:00:00
    • Data organization 00:00:00
    • Bringing it all together for root cause analysis 00:00:00
    • Security Analytics with Elastic Machine Learning 00:00:00
    • Security in the field 00:00:00
    • Threat hunting architecture 00:00:00
    • Investigation analytics 00:00:00
    • Alerting on ML Analysis 00:00:00
    • Results presentation 00:00:00
    • The results index 00:00:00
    • Alerts from the Machine Learning UI in Kibana 00:00:00
    • Creating ML alerts manually 00:00:00
    • Using Elastic ML Data in Kibana Dashboards 00:00:00
    • Visualization options in Kibana 00:00:00
    • Preparing data for anomaly detection analysis 00:00:00
    • Building the visualizations 00:00:00
    • Using Elastic ML with Kibana Canvas 00:00:00
    • Introduction to Canvas 00:00:00
    • Building Elastic ML Canvas slides 00:00:00
    • Forecasting 00:00:00
    • Forecasting versus prophesying 00:00:00
    • Forecasting use cases 00:00:00
    • Forecasting – theory of operation 00:00:00
    • Single time series forecasting 00:00:00
    • Forecast results 00:00:00
    • Multiple time series forecasting 00:00:00
    • ML Tips and Tricks 00:00:00
    • Job groups 00:00:00
    • Influencers in split versus non-split jobs 00:00:00
    • Using ML on scripted fields 00:00:00
    • Using one-sided ML functions to your advantage 00:00:00
    • Ignoring time periods 00:00:00
    • Don’t over-engineer the use case 00:00:00
    • ML job throughput considerations 00:00:00
    • Top-down alerting by leveraging custom rules 00:00:00
    • Sizing ML deployments 00:00:00

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