Six Choices Every AI Engineer Has to Make (and Nobody Teaches)
The article discusses six critical trade-offs that AI engineers face once their models are in production. It emphasizes that these decisions are rarely covered in academic courses, yet they significantly impact the effectiveness and cost of AI systems. By exploring these choices, the article aims to provide insights and frameworks to help engineers navigate the complexities of real-world AI deployment.
- ▪AI engineers must make decisions about automation versus human oversight after their models go live.
- ▪Trade-offs include build vs. buy, model complexity vs. maintainability, and data quantity vs. quality.
- ▪Understanding these trade-offs can help teams avoid exceeding budgets and improve system performance.
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Artificial Intelligence Six Choices Every AI Engineer Has to Make (and Nobody Teaches) The production trade-offs that only appear once your model is live. Sara Nobrega May 18, 2026 10 min read Share Image generated with DALL-E. University courses teach you how to make a model accurate. They rarely teach you the decisions that come right after. How do you know when to fully automate something versus keeping a human in the loop? When does prompting stop being enough and fine-tuning become worth the cost? What does it actually mean to pick real-time inference over batch when the bill arrives? These questions don’t show up in coursework. They show up your first week in production! This article walks through 6 trade-offs that show up in production AI work.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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