AI Strategy for Senior Leaders

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]

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]

Unlock the power of Retrieval Augmented Generation (RAG) to build more accurate and context-aware AI applications. This advanced course delves into the intricacies of RAG architecture, guiding you through the process of designing, implementing, and optimizing RAG pipelines for superior performance. Learn how to combine the strengths of pre-trained language models with external knowledge sources to overcome the limitations of traditional generative AI. Master the art of crafting RAG systems that deliver precise and relevant information, enhancing the user experience and driving better outcomes. Explore advanced techniques for indexing, retrieval, and generation, and discover how to tailor your RAG architecture to specific use cases and data sets. Elevate your AI skills and build cutting-edge applications that bridge the gap between knowledge and creation. Ready to transform your AI projects? Dive into RAG and build intelligent systems that not only generate content but also understand and leverage the world around them.