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Multimodal AI Systems

From CLIP to GPT-4V. Build models that see, hear and read

5 phases. 25 lessons. 25 labs. 1 capstone. Multimodal AI from vision-language models to end-to-end systems — multimodal foundations (CLIP, BLIP, image-text alignment), vision-language models (LLaVA, GPT-4V architecture, visual instruction tuning), multimodal generation (text-to-image, image editing, audio-visual generation), and production multimodal systems (RAG with images, multimodal agents, evaluation & deployment). You build a multimodal AI system that processes text, images, and audio together.

5 phases · 25 lessons · 25 labs · 1 capstone

Take Transformers & LLMs from Scratch first — this course builds on it.

Outcomes you will have by the end

• A multimodal AI system — A complete system that processes text, images, and audio together — with cross-modal retrieval, vision-language reasoning, and multimodal generation. A unified AI that sees, hears, and reads.
• A working mental model of how AI combines modalities — CLIP alignment, LLaVA projection, multimodal RAG — you understand how different modalities are embedded, aligned, and processed together in modern AI systems.
• Experience with the full multimodal stack — From CLIP to LLaVA to GPT-4V-style reasoning to multimodal agents — you've built the components that make up modern multimodal AI systems.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the CLIP, VLM, multimodal RAG, and system design skills you built.

What you will be able to do

CLIP & Contrastive Image-Text Learning · BLIP & BLIP-2 for Vision-Language · Cross-Modal Retrieval · LLaVA Architecture (Vision Encoder + LLM) · Visual Instruction Tuning · GPT-4V-Style Multimodal Reasoning · Multimodal Generation (Text-to-Image, Image Editing) · Multimodal RAG · Multimodal Agents · Multimodal System Evaluation

Every phase, every lesson, every project

• Multimodal Foundations (5 lessons) — free — Processing multiple modalities, fusion strategies, and alignment.
  • Vision Transformers and the Patch-Token Primitive
  • CLIP and Contrastive Vision-Language Pretraining
  • From CLIP to BLIP-2 — Q-Former as Modality Bridge
  • Flamingo and Gated Cross-Attention for Few-Shot VLMs
  • LLaVA and Visual Instruction Tuning
• Vision-Language Models (5 lessons) — CLIP, Flamingo, and vision-language pretraining.
  • Any-Resolution Vision: Patch-n'-Pack and NaFlex
  • Open-Weight VLM Recipes: What Actually Matters
  • LLaVA-OneVision: Single-Image, Multi-Image, Video in One Model
  • Qwen-VL Family and Dynamic-FPS Video
  • InternVL3: Native Multimodal Pretraining
• Audio-Visual Models (5 lessons) — Audio-visual fusion, video understanding, and multimodal generation.
  • Chameleon and Early-Fusion Token-Only Multimodal Models
  • Emu3: Next-Token Prediction for Image and Video Generation
  • Transfusion: Autoregressive Text + Diffusion Image in One Transformer
  • Show-o and Discrete-Diffusion Unified Models
  • Janus-Pro: Decoupled Encoders for Unified Multimodal Models
• Multimodal Generation (5 lessons) — Text-to-image, text-to-video, and multimodal generation.
  • MIO and Any-to-Any Streaming Multimodal Models
  • Video-Language Models: Temporal Tokens and Grounding
  • Long-Video Understanding at Million-Token Context
  • Audio-Language Models: the Whisper to Audio Flamingo 3 Arc
  • Omni Models: Qwen2.5-Omni and the Thinker-Talker Split
• Multimodal Deployment (5 lessons) — Serving multimodal models, optimization, and real-world applications.
  • Embodied VLAs: RT-2, OpenVLA, π0, GR00T
  • Document and Diagram Understanding
  • ColPali and Vision-Native Document RAG
  • Multimodal RAG and Cross-Modal Retrieval
  • Multimodal Agents and Computer-Use (Capstone)

The technologies you will use

PyTorch · Hugging Face Transformers · CLIP · LLaVA · Diffusers

Roles this course prepares you for

• Multimodal AI Engineer ($140k–$220k) — Build AI systems that process multiple modalities — text, images, audio — together. Understanding CLIP, LLaVA, and multimodal architectures from scratch means you can build systems that reason across modalities.
• Vision-Language Engineer ($130k–$200k) — Build vision-language models — image captioning, VQA, visual chat. Understanding the connection between vision encoders and LLMs means you can build and fine-tune VLMs for specific domains.
• AI Systems Architect ($150k–$230k) — Design end-to-end AI systems that combine multiple modalities — search, reasoning, generation, and agents. Understanding the full multimodal stack means you can architect production systems.

What multimodal AI systems actually is

It's building AI that processes multiple modalities — text, images, audio — together. From CLIP that aligns vision and language to LLaVA that connects vision encoders to LLMs to multimodal agents that use tools across modalities, you build the systems that represent the frontier of AI.

What you do every day

You build multimodal AI systems — connecting vision encoders to LLMs, building cross-modal retrieval, and creating agents that reason across modalities. When a VLM gives wrong answers about an image, you know whether it's the vision encoder, the projection layer, or the LLM reasoning.

Why companies hire for this

Multimodal AI is the frontier — GPT-4V, Gemini, Claude with vision. Companies building AI products need engineers who understand how to combine modalities, not just use single-modality models. The engineer who built LLaVA from scratch is the one who can customize VLMs for specific domains and build production multimodal systems.

What this course is not

It is not a tutorial on using GPT-4V's API. It is not about prompt engineering for vision models. It is about building the multimodal systems themselves — CLIP, LLaVA, multimodal RAG, multimodal agents — from scratch. If you want to understand how AI combines seeing, hearing, and reading, this is the course.

Common questions

Do I need Transformers & LLMs from Scratch first?

Yes — this course uses transformers, LLMs, and attention mechanisms throughout. Transformers & LLMs from Scratch builds those foundations; this course extends them to multimodal settings.

Should I take Computer Vision Engineering and NLP & Speech Processing first?

Not required, but helpful. This course focuses on the intersection of modalities — how vision and language are aligned and combined. If you want deep expertise in a single modality, take the specialized courses first. If you want to understand how modalities work together, this course is self-contained with the transformers prerequisite.

How is this different from the multimodal content in other courses?

Computer Vision Engineering covers CLIP and ViTs from a vision perspective. NLP & Speech Processing covers text and audio from a language perspective. This course focuses on the intersection — how vision encoders connect to LLMs (LLaVA), how multimodal RAG works, and how to build production systems that process multiple modalities together.

What do I end up with?

A multimodal AI system that processes text, images, and audio together — with cross-modal retrieval, vision-language reasoning, and multimodal generation. Plus the ability to architect any multimodal AI system from research papers.

Key terms in this course

RAG (Retrieval-Augmented Generation)

Continue your learning path

Transformers & LLMs from Scratch · Computer Vision Engineering · NLP & Speech Processing · Generative AI Fundamentals · ML & AI Engineering

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