Learning Objectives学习目标

1. Understand Core Principles: Gain a deep understanding of the system design principles that support modern AI applications (especially Large Language Models) and systematically master the key full-stack technologies, from the underlying hardware architecture and programming paradigms to the upper-level deep learning frameworks and compilers. 理解核心原理：深入理解支撑现代 AI 应用（尤其是大语言模型）的系统设计原则，并系统掌握从底层硬件架构与编程范式到上层深度学习框架和编译器的全栈关键技术。
2. Master Optimization Techniques: Learn and master key performance optimization methods for machine learning systems, including how to effectively scale computation, reduce memory footprint, and perform efficient task offloading and scheduling on heterogeneous computing resources (such as CPUs, GPUs, and NPUs). 掌握优化技术：学习并掌握机器学习系统的关键性能优化方法，包括如何有效扩展计算、降低内存占用，以及在 CPU、GPU、NPU 等异构计算资源上进行高效任务卸载与调度。
3. Develop Practical Skills: Through theoretical study and a series of hands-on projects, master the systematic methodology for transforming AI models into stable and efficient production-grade services, and possess the ability to design, implement, and deploy modern machine learning systems. 培养实践能力：通过理论学习和动手项目，掌握将 AI 模型转化为稳定高效生产级服务的系统方法，并具备设计、实现和部署现代机器学习系统的能力。
4. Connect with Cutting-Edge Fields: Through the study of frontier case studies such as the training and servicing of Large Language Models (LLMs), students will become familiar with the latest technologies and challenges in the industry, laying a solid foundation of knowledge and skills for future application and research in the field of machine learning systems. 连接前沿领域：通过学习大语言模型训练与服务等前沿案例，了解工业界最新技术与挑战，为未来在机器学习系统领域的应用与研究打下坚实基础。