Building Custom Multimodal AI Models with Open-Source Frameworks

• Strong understanding of machine learning and deep learning concepts
• Experience with AI frameworks like PyTorch or TensorFlow
• Familiarity with text, image, and audio data processing

Audience

• AI developers
• Machine learning engineers
• Researchers

Multimodal AI integrates multiple data types, such as text, images, and audio, to enhance machine learning models and applications.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI developers, machine learning engineers, and researchers who wish to build custom multimodal AI models using open-source frameworks.

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

• Understand the fundamentals of multimodal learning and data fusion.
• Implement multimodal models using DeepSeek, OpenAI, Hugging Face, and PyTorch.
• Optimize and fine-tune models for text, image, and audio integration.
• Deploy multimodal AI models 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 Multimodal AI

• Overview of multimodal AI and real-world applications
• Challenges in integrating text, image, and audio data
• State-of-the-art research and advancements

Data Processing and Feature Engineering

• Handling text, image, and audio datasets
• Preprocessing techniques for multimodal learning
• Feature extraction and data fusion strategies

Building Multimodal Models with PyTorch and Hugging Face

• Introduction to PyTorch for multimodal learning
• Using Hugging Face Transformers for NLP and vision tasks
• Combining different modalities in a unified AI model

Implementing Speech, Vision, and Text Fusion

• Integrating OpenAI Whisper for speech recognition
• Applying DeepSeek-Vision for image processing
• Fusion techniques for cross-modal learning

Training and Optimizing Multimodal AI Models

• Model training strategies for multimodal AI
• Optimization techniques and hyperparameter tuning
• Addressing bias and improving model generalization

Deploying Multimodal AI in Real-World Applications

• Exporting models for production use
• Deploying AI models on cloud platforms
• Performance monitoring and model maintenance

Advanced Topics and Future Trends

• Zero-shot and few-shot learning in multimodal AI
• Ethical considerations and responsible AI development
• Emerging trends in multimodal AI research

Summary and Next Steps