End-to-End Multimodal AI: Fine-Tuning, Fusion, and MLOps
By External Provider
About this course
Build production-ready multimodal AI systems that combine vision, language, and audio into unified intelligent applications. This course takes you through the full lifecycle of multimodal model development — from constructing and fine-tuning transformer-based architectures using PyTorch and TensorFlow, to diagnosing training failures, designing cross-modal retrieval systems, and deploying secure, monitored inference APIs. You will work with real-world tools including CLIP, ViT, FAISS, FastAPI, MLflow, and Ray Tune to build systems that process and integrate multiple data types simultaneously. You will analyze computational complexity to optimize fusion algorithms, evaluate model errors to identify failure patterns, and translate model outputs into stakeholder-ready business insights. This course is built for intermediate practitioners in machine learning and AI who want to move beyond single-modality models and into the cutting edge of AI systems design. By the end, you will have a portfolio of deployable, optimized multimodal systems that demonstrate advanced engineering capability to employers.
What you will learn
- Fine-tune transformer-based multimodal models using transfer learning in PyTorch and TensorFlow.
- Build cross-modal retrieval systems using FAISS and attention-based fusion of visual and text embeddings.
- Automate ML pipelines with drift monitoring, hyperparameter tuning, and retraining using MLflow and Ray Tune.
- Design and document versioned multimodal inference APIs with FastAPI, OAuth2, and OpenAPI specifications.