Building an AI Data Analyst: The Engineering Nightmares Nobody Warns You About
Published:Dec 28, 2025 11:00
•1 min read
•r/learnmachinelearning
Analysis
This article highlights a crucial aspect often overlooked in the AI hype: the significant engineering effort required to bring AI models into production. It emphasizes that model development is only a small part of the overall process, with the majority of the work involving building robust, secure, and scalable infrastructure. The mention of table-level isolation, tiered memory, and specialized tools suggests a focus on data security and efficient resource management, which are critical for real-world AI applications. The shift from prompt engineering to reliable architecture is a welcome perspective, indicating a move towards more sustainable and dependable AI solutions. This is a valuable reminder that successful AI deployment requires a strong engineering foundation.
Key Takeaways
Reference
“Building production AI is 20% models, 80% engineering.”