Automate Customer Ticket Handling - From an Idea to a Production Ready Model

Overview

Syllabus

• Introduction to AI in Customer Service
Overview of AI applications in customer support
Benefits of automating ticket handling
• Understanding Customer Ticket Data
Types of customer tickets and data formats
Data collection and preprocessing techniques
• Defining the Project Scope and Requirements
Identifying business requirements and success criteria
Setting project goals and scope
• Designing the AI Model
Basics of natural language processing (NLP)
Selecting algorithms and frameworks for ticket classification
• Data Preparation and Feature Engineering
Data cleaning and normalization
Feature extraction and selection
• Building and Training the Model
Model selection (e.g., decision trees, neural networks)
Training and validation techniques
• Evaluating Model Performance
Metrics for model accuracy and efficiency
Techniques for testing and improving model performance
• Handling Edge Cases and Model Retraining
Identifying and managing exceptions
Continuous learning and model updates
• Deploying the Model
Setting up the environment for deployment
Integrating with existing ticketing systems
• Monitoring and Maintenance
Tools and techniques for model monitoring
Strategies for regular maintenance and updates
• Case Studies and Best Practices
Real-world examples of automated ticket handling
Lessons learned and best practices
• Conclusion and Future Trends
Reviewing project achievements and challenges
Emerging trends in customer service automation
• Final Project
Developing a personalized project based on course content
Presentation and peer review of project outcomes

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Subjects

Conference Talks