The article presents a comprehensive resource for AI engineering, emphasizing the importance of building reliable and efficient AI systems. It differentiates AI engineering from basic AI development, focusing on the need for robust systems that work seamlessly in real-world applications. The initiative aims to establish AI engineering as a core discipline alongside software engineering, highlighting the necessity of engineers who can transform concepts into dependable systems. At the heart of this resource is an open learning stack that includes a textbook, software tools like TinyTorch, and hardware kits for practical experience. The textbook offers foundational knowledge on machine learning systems, while TinyTorch allows users to build their own machine learning frameworks. Users can choose to read the textbook, engage in hands-on activities, or deploy projects using various hardware kits such as Arduino and Raspberry Pi. The curriculum is designed to teach critical skills in understanding trade-offs, implementing algorithms, and facing real-world constraints. The article outlines specific learning paths: "READ" focuses on understanding machine learning concepts, "BUILD" emphasizes the implementation of frameworks, and "DEPLOY" deals with engineering under actual hardware limitations. There are also plans for competitive events like the "AI Olympics," aimed at university teams and public leaderboards, set to launch in 2026. Importantly, the initiative aspires to reach one million learners by 2030, fostering a community that values rigorous AI engineering practices. The resource will remain dynamic, continuously updated to reflect advancements in the field.