大模型时代的AI能力工程化

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The transition to AI 2.0 is marked by lower costs for AI applications and broader application ranges, driven by advancements in foundational models and self-supervised learning [4][5] - The report emphasizes the importance of AI capability engineering, highlighting the need for a comprehensive AI development and operational system [22][46] - There is a significant focus on the challenges of AI model governance, including difficulties in management, deployment, monitoring, and collaboration [20][21] Summary by Sections AI Development and Application - AI applications are becoming more cost-effective, allowing for a wider range of use cases [4] - The emergence of foundational models enables low-cost fine-tuning for various domain tasks [4][5] - A staggering 87% of AI model development projects do not reach production, indicating inefficiencies in deployment processes [13] AI Governance and Collaboration - AI model governance remains inconsistent, with common challenges in managing complex data and ensuring effective collaboration among teams [20][21] - The report identifies the need for standardized processes in MLOps and LLMOps to enhance model governance [22] Future Directions and Strategies - The report outlines a vision for AI 2.0, focusing on the integration of AI-driven digital products, unified platforms, and modern data architectures [46] - Emphasis is placed on creating a self-service AI platform that facilitates model management and fine-tuning, enhancing operational efficiency [41][46] - The need for responsible technology and a culture that embraces agile delivery and product thinking is highlighted as essential for successful AI transformation [46][47]