Fine-Tuning Multimodal Models

• Proficiency in Python programming
• Understanding of deep learning concepts
• Experience with fine-tuning pre-trained models

Audience

• AI researchers
• Data scientists
• Machine learning practitioners

Fine-Tuning Multimodal Models focuses on advanced techniques for adapting models that process multiple data types, such as text, images, and videos. Participants will gain insights into handling complex datasets, optimizing model performance, and deploying these models for real-world applications, such as visual question answering and content generation.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to master multimodal model fine-tuning for innovative AI solutions.

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

• Understand the architecture of multimodal models like CLIP and Flamingo.
• Prepare and preprocess multimodal datasets effectively.
• Fine-tune multimodal models for specific tasks.
• Optimize models for real-world applications and performance.

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 Models

• Overview of multimodal machine learning
• Applications of multimodal models
• Challenges in handling multiple data types

Architectures for Multimodal Models

• Exploring models like CLIP, Flamingo, and BLIP
• Understanding cross-modal attention mechanisms
• Architectural considerations for scalability and efficiency

Preparing Multimodal Datasets

• Data collection and annotation techniques
• Preprocessing text, images, and video inputs
• Balancing datasets for multimodal tasks

Fine-Tuning Techniques for Multimodal Models

• Setting up training pipelines for multimodal models
• Managing memory and computational constraints
• Handling alignment between modalities

Applications of Fine-Tuned Multimodal Models

• Visual question answering
• Image and video captioning
• Content generation using multimodal inputs

Performance Optimization and Evaluation

• Evaluation metrics for multimodal tasks
• Optimizing latency and throughput for production
• Ensuring robustness and consistency across modalities

Deploying Multimodal Models

• Packaging models for deployment
• Scalable inference on cloud platforms
• Real-time applications and integrations

Case Studies and Hands-On Labs

• Fine-tuning CLIP for content-based image retrieval
• Training a multimodal chatbot with text and video
• Implementing cross-modal retrieval systems

Summary and Next Steps