2024年AIReady的数据基础设施参考架构白皮书

Investment Rating - The report does not explicitly state an investment rating for the industry or company. Core Insights - The AI large model is accelerating the intelligent transformation of various industries, with applications becoming increasingly widespread and enhancing operational efficiency and decision-making capabilities [19][21][25]. - The need for AI-Ready data infrastructure is emphasized, as it is crucial for supporting the training and deployment of AI models, ensuring high performance and data availability [20][22][34]. - The report highlights the importance of data quality and management, stating that 80% of the time in AI workflows is spent preparing high-quality data, which is essential for effective model training [36][40]. Summary by Sections AI Large Model Applications - AI large models are penetrating various sectors, achieving breakthroughs in coverage and precision, and enhancing generalization capabilities [21][25]. - The emergence of models like ChatGPT and advancements in video generation models signify a shift towards more complex AI applications [22][23]. Data Infrastructure Requirements - AI large models require robust data infrastructure characterized by high performance, strong consistency, and the ability to handle massive data volumes efficiently [13][17][34]. - The report defines "AI-Ready" data infrastructure as essential for the effective operation of AI models, focusing on aspects like scalability, flexibility, and real-time data access [11][12][34]. Challenges in Data Management - The report identifies significant challenges in data asset management, including data quality, standardization, and the prevalence of data silos, which hinder effective AI model training [36][40][41]. - Recommendations include establishing unified data management platforms and creating global AI data lakes to enhance data accessibility and quality [39][41]. Industry-Specific Applications - Various industries, including finance, healthcare, and manufacturing, are leveraging AI large models for applications such as risk assessment, personalized services, and operational efficiency [25][26][32]. - The report outlines specific use cases in banking, healthcare, and public services, demonstrating the transformative potential of AI technologies across sectors [25][26][32].