Multimodal AI with DeepSeek Training in Dublin, Ireland

The Multimodal AI with DeepSeek Training is a hands-on, advanced program designed to teach how to build and use AI systems that understand and combine text, images, documents, and other data types using DeepSeek multimodal models.

Participants will learn to design multimodal pipelines for document intelligence, vision + language applications, cross-modal search, and enterprise AI use cases, enabling richer, more intelligent AI solutions.

Introduction to Multimodal AI

• What is multimodal AI and why it matters

• Text-only vs multimodal models

• Overview of DeepSeek multimodal capabilities

DeepSeek Multimodal Models & APIs

• Available DeepSeek multimodal models

• API usage and authentication

• Input/output formats for images and documents

• Error handling and limitations

Image Understanding & Vision-Language Tasks

• Image classification and description

• Visual question answering (VQA)

• Object and scene understanding

• Image-based reasoning use cases

Document Intelligence with Multimodal AI

• Processing PDFs and scanned documents

• Extracting tables, forms, and layouts

• Combining OCR + language understanding

• Structured document extraction

Text + Image + Data Fusion

• Combining visual and textual context

• Cross-modal reasoning

• Linking images to structured data

• Multimodal feature engineering

Multimodal RAG (Retrieval-Augmented Generation)

• Multimodal embeddings and search

• Image + document retrieval

• Grounding multimodal responses

• Reducing hallucinations with retrieval

Multimodal Workflows & Pipelines

• Designing end-to-end multimodal pipelines

• Orchestrating multiple model calls

• Handling large files and batching

• Workflow automation patterns

Performance, Cost & Scalability

• Optimizing multimodal requests

• Caching and reuse strategies

• Managing latency for vision tasks

• Cost control and monitoring

Security, Privacy & Data Governance

• Handling sensitive images and documents

• Access control and data retention

• Compliance considerations

• Responsible multimodal AI use

Testing & Validation for Multimodal Systems

• Evaluating multimodal outputs

• Ground truth and validation datasets

• Error analysis for vision-language systems

• Regression testing for pipelines

Deployment & Production Operations

• Deploying multimodal AI services

• CI/CD for multimodal pipelines

• Monitoring and observability

• Versioning models and workflows

Multimodal UX & Application Design

• Designing user interfaces for multimodal input

• User feedback and correction loops

• Explainability and transparency

• Human-in-the-loop patterns

Advanced Topics & Future Trends

• Video + language models (conceptual)

• Audio + vision + text convergence

• Edge multimodal AI (conceptual)

• Emerging multimodal research directions

Hands-on Exercises

Summary and Conclusion