AI in Production
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
AI in Production
-
ナレーター：
-
著者：
概要
In this episode, we explore what happens when AI leaves the lab and enters real-world production. We examine why most AI projects fail at deployment, how production systems differ fundamentally from research models, and what it takes to operate large language models reliably at scale.
The discussion focuses on the engineering, organizational, and governance challenges of deploying probabilistic systems, along with the emerging architectures that turn LLMs into agents capable of planning, tool use, and autonomous action.
This episode covers:
- Why most AI projects fail in production
- Research vs. production AI: reliability, consistency, and scale
- Build vs. buy trade-offs for LLMs
- Hidden costs: prompt drift, prompt engineering, and inference
- Evaluation, monitoring, and governance in real systems
- Agent architectures and AI as infrastructure
This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.
Sources and Further Reading
Additional references and extended material are available at:
https://adapticx.co.uk