Multi-Modal AI Agents: Integrating Text, Image, and Speech

• An understanding of machine learning fundamentals
• Experience with Python programming
• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)

Audience

• AI developers
• Researchers
• Multimedia engineers

Multi-modal AI agents are transforming human-computer interaction by integrating text, images, speech, and video processing capabilities.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level AI developers, researchers, and multimedia engineers who wish to build AI agents capable of understanding and generating multi-modal content.

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

• Develop AI agents that process and integrate text, image, and speech data.
• Implement multi-modal models such as GPT-4 Vision and Whisper ASR.
• Optimize multi-modal AI pipelines for efficiency and accuracy.
• Deploy multi-modal AI agents in real-world applications.

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.

Introduction to Multi-Modal AI

• What is multi-modal AI?
• Key challenges and applications
• Overview of leading multi-modal models

Text Processing and Natural Language Understanding

• Leveraging LLMs for text-based AI agents
• Understanding prompt engineering for multi-modal tasks
• Fine-tuning text models for domain-specific applications

Image Recognition and Generation

• Processing images with AI: classification, captioning, and object detection
• Generating images with diffusion models (Stable Diffusion, DALLE)
• Integrating image data with text-based models

Speech and Audio Processing

• Speech recognition with Whisper ASR
• Text-to-speech (TTS) synthesis techniques
• Enhancing user interaction with voice-based AI

Integrating Multi-Modal Inputs

• Building AI pipelines for processing multiple input types
• Fusion techniques for combining text, image, and speech data
• Real-world applications of multi-modal AI agents

Deploying Multi-Modal AI Agents

• Building API-driven multi-modal AI solutions
• Optimizing models for performance and scalability
• Best practices for deploying multi-modal AI in production

Ethical Considerations and Future Trends

• Bias and fairness in multi-modal AI
• Privacy concerns with multi-modal data
• Future developments in multi-modal AI

Summary and Next Steps