收集“龙珠”，亚马逊云科技要召唤什么？

Group 1 - Amazon Web Services (AWS) announced the launch of the Qwen3 series and DeepSeek-V3.1 models on Amazon Bedrock, marking Alibaba as the 13th major AI model provider on the platform [2] - The Qwen3 model, released by Alibaba in April 2023, significantly enhances capabilities in reasoning, instruction following, tool usage, and multilingual support, setting new performance records for domestic and global open-source models [3] - AWS emphasizes a diverse customer base, serving both large contracts and small startups, reflecting its strategy to provide foundational technology for organizations of all sizes [4] Group 2 - The "Choice Matters" strategy aims to provide suitable models for different business scenarios rather than solely focusing on popularity or performance [5] - AWS has expanded the availability of models across multiple continents, enhancing the ecosystem of open-weight models and offering customers a variety of high-quality options [5] - A significant trend is the shift from merely selecting models to focusing on how to effectively implement AI systems in production, addressing challenges in technology maturity and operational readiness [6][17] Group 3 - To improve the success rate of AI projects, AWS identifies three key elements: scenario assessment, technology selection, and systematic thinking [7] - A multi-dimensional evaluation system is necessary for project feasibility, considering business impact, risk control, data support, budget, and team configuration [8] - The ROI should guide every stage of the AI project lifecycle, ensuring alignment between technological innovation and business value [10] Group 4 - Companies are encouraged to maintain an open learning mindset and continuously optimize their AI capabilities through experimentation and iteration [12] - AWS provides a comprehensive landscape for generative AI technology selection, focusing on model, data, and technology route dimensions [13] - The trend is moving towards a flexible "scenario adaptation" strategy, allowing for the use of multiple models to optimize performance in complex scenarios [15] Group 5 - Data governance is crucial for the sustainable application of generative AI, requiring robust data quality standards, privacy measures, and access controls [16] - The development of Agentic AI is becoming a significant direction, with two main implementation paths: Model-Chain-Pipeline and Agent Workflow [16] - The competitive edge for companies lies not in model selection but in effectively transitioning systems to production [17]