LLM + VLM Integration: Multimodal AI Systems

LLM + VLM Integration: Multimodal AI Systems is an intermediate-to-advanced course for engineers who want to build production-grade systems that understand and reason over text, images, and structured data in a unified way. You will learn how modern Large Language Models (LLMs) and Vision-Language Models (VLMs) are architected, how they align across modalities, and how to design real-time, scalable applications that leverage both. By the end, you will be able to design, train, fine-tune, evaluate, and deploy end-to-end multimodal applications.

This course goes far beyond basic overviews. We dive deeply into transformer internals, attention mechanisms, tokenizer and vocabulary design, scaling laws, inference optimization, and parameter-efficient fine-tuning such as LoRA and QLoRA. On the vision side, you will explore ViT and CNN-based encoders, visual embedding construction, contrastive objectives like InfoNCE, and prevalent VLM families such as CLIP, LLaVA, and Qwen-VL. You will understand cross-modal alignment and fusion—early, mid, and late fusion strategies; cross-attention for interaction; attention pooling; and synchronization across variable-length inputs.

For practitioners building real systems, we cover the complete pipeline: multimodal preprocessing, unified embedding spaces, retrieval in joint latent spaces, and response generation grounded in both text and vision. You will implement low-latency inference using batching, KV-cache reuse, asynchronous processing, and GPU scheduling. We also tackle monitoring, observability, fault isolation, and cost-aware scaling across multi-GPU and Kubernetes environments using frameworks such as vLLM and Ray Serve.

Who is this course for?

• Advanced ML engineers and AI architects who need to ship reliable multimodal products.
• Computer vision specialists expanding into language understanding and grounding.
• NLP engineers adding robust visual understanding and visual grounding.
• Technical leaders and researchers focused on next-generation multimodal AI systems.

What you will learn

• Deep understanding of LLM and VLM architectures, attention, and training dynamics.
• Fusion and alignment mechanisms across modalities, including cross-attention and projection heads.
• Design of unified multimodal pipelines, from preprocessing to inference and feedback loops.
• Real-time processing of mixed text, image, and video inputs with strict latency requirements.
• Knowledge integration and cross-modal reasoning with grounding and fact verification.
• Efficient fine-tuning strategies, multi-task training, and evaluation protocols.
• Production deployment, monitoring, cost optimization, and horizontal scaling.

Prerequisites include strong familiarity with transformers, PyTorch, and modern ML systems. Throughout the course, you will practice with realistic scenarios such as document understanding, visual question answering, multimodal retrieval, captioning, and real-time chatbots that combine text and vision. You will learn to select the right architecture and fusion strategy for each task, measure quality with appropriate metrics (from language perplexity to CLIP retrieval scores and VQA accuracy), and harden your systems for real-world edge cases and failures.

By completion, you will be able to design and implement end-to-end multimodal solutions, fine-tune unified models with parameter-efficient techniques, reason across modalities, and deploy at scale with robust observability and cost control. This course equips you with the patterns, trade-offs, and practical tooling to build reliable multimodal AI systems ready for production.