Science of Watermarking AI

This course details the critical techniques for optimising AI model inference performance. Learners will understand how to apply quantization, model pruning, and hardware-specific acceleration to significantly reduce latency and computational cost for deploying large language models and other AI systems. After completing this course, you will be able to diagnose inference bottlenecks and implement effective strategies using tools like ONNX Runtime and NVIDIA TensorRT. [gemma]

This course equips senior leaders with the knowledge to formulate and execute effective AI strategies. You'll learn to assess AI opportunities, manage risks, and align AI initiatives with business goals, covering frameworks like the AI Transformation Playbook and real-world case studies. [gemma]

Fine-tuning large language models allows you to adapt pre-trained models to specific tasks and datasets. This course covers parameter-efficient fine-tuning techniques like LoRA, QLoRA, and adapters using libraries like Hugging Face Transformers and PEFT. You will be able to implement and evaluate fine-tuned models for improved performance on your target applications. [gemma]