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Machine Learning with the Elastic Stack

  • Course Code: Data Science - Machine Learning with the Elastic Stack
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Leverage Elastic Stack’s machine learning features to gain valuable insight from your data.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning with the Elastic Stack skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Leverage Elastic Stack’s machine learning features to gain valuable insight from your data. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Install the Elastic Stack to use machine learning features 
  • Understand how Elastic machine learning is used to detect a variety of anomaly types 
  • Apply effective anomaly detection to IT operations and security analytics 
  • Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting 
  • Combine your created jobs to correlate anomalies of different layers of infrastructure 
  • Learn various tips and tricks to get the most out of Elastic machine learning 
  • Install the Elastic Stack to use machine learning features 
  • Understand how Elastic machine learning is used to detect a variety of anomaly types 
  • Apply effective anomaly detection to IT operations and security analytics 
  • Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting 
  • Combine your created jobs to correlate anomalies of different layers of infrastructure 
  • Learn various tips and tricks to get the most out of Elastic machine learning 

Audience & Pre-Requisites 

This course is designed for developers wants Leverage Elastic Stack’s machine learning features to gain valuable insight from your data 

Pre-Requisites:  Students should have familiar with  

  • Basics of ML 
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Machine Learning for IT 
  • Machine Learning for IT 
  • Overcoming the historical challenges 
  • Theory of operation 
  • Operationalization 
  • Supporting indices 
  • The orchestration 
  1. Installing the Elastic Stack with Machine Learning 
  • Installing the Elastic Stack with Machine Learning 
  • Installing the Elastic Stack 
  • A guided tour of Elastic ML features 
  1. Event Change Detection 
  • Event Change Detection 
  • 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 
  1. IT Operational Analytics and Root Cause Analysis 
  • IT Operational Analytics and Root Cause Analysis 
  • Holistic application visibility 
  • Data organization 
  • Bringing it all together for root cause analysis 
  1. Security Analytics with Elastic Machine Learning 
  • Security Analytics with Elastic Machine Learning 
  • Security in the field 
  • Threat hunting architecture 
  • Investigation analytics 
  1. Alerting on ML Analysis 
  • Alerting on ML Analysis 
  • Results presentation 
  • The results index 
  • Alerts from the Machine Learning UI in Kibana 
  • Creating ML alerts manually 
  1. Using Elastic ML Data in Kibana Dashboards 
  • Using Elastic ML Data in Kibana Dashboards 
  • Visualization options in Kibana 
  • Preparing data for anomaly detection analysis 
  • Building the visualizations 
  1. Using Elastic ML with Kibana Canvas 
  • Using Elastic ML with Kibana Canvas 
  • Introduction to Canvas 
  • Building Elastic ML Canvas slides 
  1. Forecasting 
  • Forecasting 
  • Forecasting versus prophesying 
  • Forecasting use cases 
  • Forecasting – theory of operation 
  • Single time series forecasting 
  • Forecast results 
  • Multiple time series forecasting 
  1. ML Tips and Tricks 
  • ML Tips and Tricks 
  • 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 
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