This course introduces to Splunk AIOps, covering the essentials of data analysis, security management, and AI-driven operations. The training will present the basics of Splunk, advanced data analysis techniques, machine learning models, and security essentials. Participants will learn how to integrate AI into everyday workflows, enhance service performance, and improve efficiency across various operational teams.
In today's data-driven landscape, organizations are constantly seeking innovative solutions to extract actionable insights, enhance operational efficiency, and bolster security measures. Splunk AI emerges as a transformative tool that empowers companies to harness the power of artificial intelligence (AI) to optimize their operations and drive business success. By leveraging Splunk AI, organizations can unlock many benefits, including advanced data analysis, predictive capabilities, streamlined security operations, and improved decision-making processes. This integration of AI within the Splunk platform not only enhances the effectiveness of data analysis but also enables companies to proactively address challenges, mitigate risks, and deliver superior services to their customers. Through Splunk AI, companies can work smarter, faster, and more effectively, reducing operating costs and positioning themselves at the forefront of innovation and competitiveness in today's dynamic business environment.
Prerequisites:
The knowledge and skills that a learner should have before attending this course are as follows:
- Familiarity with Cisco Products
Course Objectives:
Upon completing this course, the learner will be able to meet these overall objectives:
Understand the basics of Splunk and its core software
Develop skills in collecting, indexing, and analyzing machine data using Splunk
Introduce Splunk data analysis and correlation techniques to enable fast decision making, cost reduction and better customer services in your environment
Describe machine learning models and their applications for various tasks using Splunk
Introduce Splunk’s feature engineering capability
Discuss Splunk Security Essentials and Incident Response for security management
Explore the role of Splunk AIOps in supporting Observability in IT Operations
Get Hands-on experience in setting up, configuring, and managing a Splunk environment
Apply Splunk AI capabilities to real-world use cases and scenarios
Module 1: AI Operations Fundamentals
Understanding AIOps: What it is and why it matters.
Role of AI in IT operations.
Overview of Splunk’s AIOps capabilities.
Module 2: Splunk Essentials
Introduction to Splunk and its core software.
Navigating the Splunk interface.
Splunk interoperability.
Data ingestion and indexing.
Basic search queries and visualization.
Module 3: Data Preparation and Cleansing using Splunk
Benefits for the companies using Splunk AI engine in cost reduction and operational efficiency improvements.
Data quality and preprocessing.
Handling missing values and outliers.
Data analysis and correlation techniques in Splunk
Module 4: Cisco Splunk AIOps Use Cases
Data modeling in Splunk.
Overview of AIOps and its role in IT Operations.
Data analysis and correlation techniques in Splunk.
Predictive analytics for incident prevention.
Anomaly detection and root cause analysis.
Full visibility, AIOps, and incident intelligence with Splunk AI.
Module 5: Machine Learning with Splunk
Introduction to ML algorithms.
Machine learning models for classification, regression, clustering, and anomaly detection.
Building Predictive Models using Splunk ML Toolkit
- Implementing real-time search, analysis, and visualization of large datasets.
- Event correlation techniques using search commands.
- Clustering and anomaly detection.
Module 7: Operationalizing AIOps with Splunk
- Integrating AIOps insights into incident management.
- Splunk Security Essentials and Incident Response.
- Real-time monitoring and alerting using Splunk Enterprise Security.
- Testing, validating, and fine-tuning security use cases.
- Rapid event detection and human-assisted automation for SecOps, ITOps, and engineering teams.
- Automating responses based on ML predictions.
Module 8: Case Studies and Best Practices
- Success stories from organizations using Splunk for AIOps.
- Integrating AI into everyday workflows for service performance enhancement.
- Tips for effective implementation.
Lab Outline
Lab 1: Setting Up Your Splunk Environment
Install and configure Splunk.
Navigate Splunk Enterprise UI.
Lab 2: Data Analysis Introduction
Upload dataset to Splunk.
Index sample data for analysis.
Create visualizations.
Lab 3: Data Preparation
Cleanse real-world data.
Create relevant features for ML.
Lab 4: Building Your First AIOps Model using Splunk
Train a model to predict incidents.
Evaluate model performance.
Lab 5: Advanced Data Analysis
Advanced data analysis techniques.
Machine Learning Model deployment.
Security Incident Response use case.
Lab 6: Real-World AIOps Scenarios
Analyze live data streams.
Implement automated actions based on ML recommendations.
The primary audience for this course is as follows:
- System Engineers
- System Administrators
- Architects
- Channel Partners
- Data Analysts