如何让你的数据为人工智能做好准备

Core Insights - The emergence of agent-based AI is fundamentally transforming the big data paradigm, requiring a proactive approach to data integration into specialized intelligent computing platforms rather than the traditional reactive methods [1] - This shift is leading to a re-evaluation of data modeling and storage, as modern AI can leverage significantly smaller datasets compared to traditional machine learning [1] Group 1: Changes in Data Interaction - The way data is utilized is evolving, with non-technical users increasingly interacting directly with data through AI agents, moving from a builder-centric to an interactor-centric model [2][4] - Existing SaaS applications are integrating natural language interactions more seamlessly, allowing users to create applications based on their needs [4][6] Group 2: Data Engineering Principles - Data engineers must rethink ETL/ELT processes, focusing on context rather than strict normalization, as AI agents can interpret data without extensive preprocessing [7][9] - The importance of data organization is emphasized over mere data collection, as quality examples for context-based learning are more valuable than large quantities of data [10][12] Group 3: Infrastructure and Management - AI agents require infrastructure that supports both data perception and action, necessitating clear interfaces and documentation for effective tool usage [15][17] - The management of AI-generated artifacts is crucial, as these outputs become part of the data ecosystem and must adhere to industry standards and regulations [20][21] Group 4: Observability and Training - Establishing a feedback loop between observability and training is essential for enhancing AI agent performance, requiring a platform to monitor data quality and model performance [22][24] - Data engineers' roles are evolving to include maintaining decision logs and managing agent-generated code as versioned artifacts for future analysis and training [26][29]