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Multimodal AIComputer VisionAudio Engineering
Mastering Multimodal AI
Engineering Vision, Audio, and Language Fusion Systems
This course transitions students from LLM-centric thinking to Large Multimodal Model (LMM) engineering. Participants will learn to align different data distributions (pixels, waveforms, and tokens) into a shared latent space to build 'eyes, ears, and voices' for their AI applications.
What You Will Master
Understand the Alignment Problem: Why concatenation fails image/text vectors.
Master Contrastive Learning and deep dive into CLIP architecture.
Implement Joint vs. Coordinated Representations in n-dimensional space.
Build systems using Vision Transformers (ViT) and Projection Layers.
Fine-tune LMMs like BLIP-2, Flamingo, and LLaVA on custom datasets.
Integrate Audio & Speech: Raw audio vs. spectrogram representations.
Explore the 'Omni' Trend: Native audio tokens without text transcription.
Implement Early, Late, and Cross-attention fusion strategies.
Build Multimodal RAG systems using LanceDB and Milvus.
Optimize and deploy heavy multimodal pipelines in production.
Curriculum Overview
The Engineering Stack
Frameworks
PyTorch, Hugging Face
Models
CLIP, Whisper, LLaVA
Vector Search
Qdrant, Milvus, LanceDB
Deployment
NVIDIA Triton, vLLM
Multimodal Engineering Projects
Practical, production-grade projects designed to benchmark your mastery of LMM engineering.
The Interactive Concierge
A video-audio-text agent that sees, hears, and responds contextually.
LLaVA-v1.6 + Whisper + Bark
Multimodal Security Auditor
Correlates CCTV footage with audio triggers like breaking glass.
CLIP + CLAP + Milvus
Medical Diagnostic Aid
Fuses X-ray imagery with patient history and doctor's voice notes.
BioViL + Med-PALM 2 Principles
The Multimodal Shift
"The next generation of AI won't just 'read' the world; it will perceive it. Mastering the fusion of vision, audio, and language is the key to building truly autonomous systems."
Frequently Asked Questions
Mastery Assessment: Multimodal AI & Agentic Systems
Validate your expertise in MLLM architectures, modality alignment, and agentic AI security.
Multimodal AI Mastery Assessment
50 comprehensive questions covering MLLM modules, training stages, modality competition, and agentic security.
Full Lifetime Access
Professional Certification
LMM Fine-tuning Workbench
AI Engineering Community
Compute Credits Included
Instructor
Celoris Designs
Celoris Designs
Pioneering AI-First Development
Specializing in advanced AI Systems and Multimodal Engineering. We help engineers bridge the gap between text-only LLMs and complex perception-driven AI.
4.98(1850+)
8-10 Weeks (Self-paced) Content
Prerequisites
- Strong proficiency in Python and PyTorch
- Deep understanding of Transformer architectures
- Familiarity with Hugging Face ecosystem
- Experience with Vector Databases is recommended