Google AutoML Chatbots Training in Bangkok, Thailand

The Google AutoML Chatbots Training is a hands-on program designed to help teams build, train, and deploy intelligent chatbots using Google AutoML and Google Cloud conversational AI tools.

The course focuses on creating custom, scalable, and production-ready chatbots for customer support, internal helpdesks, and business automation—without requiring deep machine learning expertise.

Introduction to Google AutoML & Conversational AI

• Overview of Google AutoML for chatbots
• AutoML vs rule-based chatbots
• Common business chatbot use cases
• Benefits and limitations

Google AutoML Chatbot Architecture

• Key components in Google Cloud conversational AI
• How AutoML models fit into chatbot systems
• Training, inference, and deployment lifecycle
• Cost and scalability considerations

Conversational Design Fundamentals

• Designing user intents and goals
• Conversation flow and dialog states
• Handling user errors and fallbacks
• Creating natural and helpful conversations

Intent & Entity Modeling

• Defining intents
• Creating and managing entities
• Labeling and organizing training data
• Improving intent recognition accuracy

Training Data Preparation

• Collecting sample utterances
• Data quality and balance
• Handling edge cases
• Avoiding bias in training data

Training & Evaluating AutoML Models

• Training chatbot models
• Evaluating accuracy and confidence
• Confusion matrix and error analysis
• Iterative improvement cycles

Dialog Management & Fulfillment

• Managing multi-turn conversations
• Context handling
• Backend fulfillment logic
• Connecting to business rules

Integration with Business Systems

• Integrating with CRM, ticketing, and databases
• Webhooks and APIs
• Secure system connections
• Data flow design

Multichannel Deployment

• Deploying to web chat
• Integration with messaging platforms
• Voice vs text channels
• Consistent user experience across channels

Monitoring, Analytics & Optimization

• Monitoring chatbot performance
• Tracking user satisfaction
• Identifying drop-offs and failures
• Continuous improvement strategies

Security, Privacy & Compliance

• Handling personal and sensitive data
• Access controls and permissions
• Data retention and compliance
• Secure chatbot design

Governance & Responsible Chatbot AI

• Preventing harmful or incorrect responses
• Human escalation paths
• Transparency and user trust
• Responsible AI principles

Hands-on Exercises

Summary and Conclusion