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Duration 21 hours
Course Outline
Introduction to Conversational AI
- Historical trajectory and evolution of voice-enabled systems
- Core components: Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Dialogue Management, and Text-to-Speech (TTS)
- Overview of leading platforms including Alexa, Google Assistant, and Rasa
Designing Voice Interfaces
- Fundamental principles of conversational user experience
- Modeling user intent and extracting entities
- Utilization of voice design methodologies and process mapping
Developing with Dialogflow and Alexa
- Configuration of Dialogflow agents, intents, and webhook fulfillment mechanisms
- Development of Alexa Skills: defining intents, slots, voice models, and endpoint integration
- Management of multi-turn interactions and session state
Building Voice Assistants with Rasa
- Rasa architecture: Natural Language Understanding (NLU), Core logic, and Action handling
- Preparation of training datasets and domain configuration
- Implementation of custom actions, form handling, and context-aware dialogues
Integrating Voice Assistants
- Implementation of APIs and webhook-based backend services
- Connection to Customer Relationship Management (CRM) systems, databases, and external applications
- Deployment of voice assistants in web applications, IoT devices, and mobile platforms
Testing, Deployment, and Optimization
- Use of simulators and defined test cases for voice interaction validation
- Monitoring usage metrics and debugging conversational flows
- Deployment to Google Assistant, Alexa devices, or private enterprise platforms
Security, Compliance, and Scalability
- Implementation of user authentication and authorization protocols for assistants
- Data privacy standards, GDPR compliance, and maintenance of audit trails
- Application of version control and Continuous Integration/Continuous Deployment (CI/CD) pipelines for voice applications
Summary and Next Steps
Requirements
- Proficiency in RESTful APIs and JSON data structures
- Experience with at least one programming language (e.g., Python or JavaScript)
- Familiarity with Natural Language Processing (NLP) concepts
Audience
- Software developers
- User Experience (UX) designers specializing in voice-based interfaces
- Conversational AI teams engaged in the development of virtual assistants