Course Code: slmshai
Duration: 14 hours
Prerequisites:
  • Basic understanding of Artificial Intelligence and Machine Learning
  • Proficiency in Python programming
  • Experience with Natural Language Processing concepts

Audience

  • Data scientists
  • Machine learning engineers
  • AI researchers and developers
  • Product managers and UX designers
Overview:

Small Language Models (SLMs) are compact yet powerful tools for enabling sophisticated human-AI interactions in various applications, including conversational AI and customer service bots.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists, machine learning and AI researchers who wish to create engaging and efficient AI-powered conversational experiences with small language models.

By the end of this training, participants will be able to:

  • Understand the fundamentals of conversational AI and the role of SLMs.
  • Design and implement user-centric AI interactions.
  • Develop and train SLMs for interactive applications.
  • Evaluate and improve the effectiveness of human-AI communication using appropriate metrics.
  • Deploy scalable and ethical AI-driven conversational interfaces in real-world scenarios.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Conversational AI and Small Language Models (SLMs)

  • Fundamentals of conversational AI
  • Overview of SLMs and their advantages
  • Case studies of SLMs in interactive applications

Designing Conversational Flows

  • Principles of human-AI interaction design
  • Crafting engaging and natural dialogues
  • User experience (UX) considerations

Building Customer Service Bots

  • Use cases for customer service bots
  • Integrating SLMs into customer service platforms
  • Handling common customer inquiries with AI

Training SLMs for Interaction

  • Data collection for conversational AI
  • Training techniques for SLMs in dialogue systems
  • Fine-tuning models for specific interaction scenarios

Evaluating Interaction Quality

  • Metrics for assessing conversational AI
  • User testing and feedback collection
  • Iterative improvement based on evaluation

Voice-Enabled and Multimodal Interactions

  • Incorporating voice recognition with SLMs
  • Designing multimodal interactions (text, voice, visuals)
  • Case studies of voice assistants and chatbots

Personalization and Contextual Understanding

  • Techniques for personalizing interactions
  • Context-aware conversation handling
  • Privacy and data security in personalized AI

Ethical Considerations and Bias Mitigation

  • Ethical frameworks for conversational AI
  • Identifying and mitigating biases in interactions
  • Ensuring inclusivity and fairness in AI communication

Deployment and Scaling

  • Strategies for deploying conversational AI systems
  • Scaling SLMs for widespread use
  • Monitoring and maintaining AI interactions post-deployment

Capstone Project

  • Identifying a need for conversational AI in a chosen domain
  • Developing a prototype using SLMs
  • Testing and presenting the interactive application

Final Assessment

  • Submission of a capstone project report
  • Demonstration of a functional conversational AI system
  • Evaluation based on innovation, user engagement, and technical execution

Summary and Next Steps

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