Splunk MLTK training

• Equip participants with a foundational understanding of machine learning concepts and principles.
• Develop proficiency in using the Splunk interface for data processing, analysis, and visualization.
• Enhance skills in data ingestion, cleaning, and pre-processing to prepare data for machine learning applications.
• Explore and understand various machine learning algorithms available within the Splunk MLTK.
• Train participants in model creation, training, validation, and deployment using real-world datasets.
• Learn the intricacies of deploying, managing, and scaling machine learning models within the Splunk environment.

• Recorded Videos After Training
• Digital Learning Material
• Course Completion Certificate
• 24x7 After Training Support

• Data Analysts
• IT Professionals
• Security Analysts
• Systems Administrators
• Business Analysts
• Machine Learning Enthusiasts
• Splunk Users

• Basic Understanding of Splunk: Familiarity with the Splunk interface and basic functionalities.
• Data Analysis Knowledge: A foundational understanding of data analysis concepts and techniques.
• Statistical Knowledge: A grasp of basic statistical concepts and methodologies.
• Machine Learning Basics: An introductory understanding of machine learning principles is advantageous but not compulsory.

• Multisoft Systems provides a globally recognized training certificate after successful completion of Splunk MLTK training certification course.

Module 1: Introduction

• Overview of Splunk MLTK
• Key features and capabilities
• Installation and configuration

Module 2: Data Preparation

• Data ingestion in Splunk
• Data cleaning and pre-processing
• Feature extraction and transformation

Module 3: Basic Concepts of Machine Learning

• Supervised vs. Unsupervised Learning
• Overview of common algorithms
• Model training and evaluation metrics

Module 4: Splunk MLTK Algorithms

• Exploration of built-in algorithms
• Use cases and applications
• Custom algorithm integration

Module 5: Building Models in Splunk

• Utilizing the Splunk MLTK Assistant
• Model training and parameter tuning
• Model validation and testing

Module 6: Operationalizing Models

• Deploying machine learning models
• Scheduling and automating model retraining
• Monitoring and managing models

Module 7: Use Case Implementation

• Walkthrough of specific use cases (e.g., IT operations, security, business analytics)
• Building custom analytics and visualizations
• Integrating machine learning insights into dashboards

Module 8: Advanced Topics

• Deep learning in Splunk
• Integrating with external machine learning platforms and tools
• Scaling and optimizing machine learning in Splunk

Module 9: Best Practices

• Security and privacy considerations
• Performance optimization
• Troubleshooting and support

Module 10: Hands-on Labs and Projects

• Real-world data analysis challenges
• Implementing end-to-end machine learning solutions
• Evaluating and presenting results