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cover of episode Creating tested, reliable AI applications

Creating tested, reliable AI applications

2024/11/13
logo of podcast Practical AI: Machine Learning, Data Science, LLM

Practical AI: Machine Learning, Data Science, LLM

AI Deep Dive AI Chapters Transcript
People
C
Chris Benson
C
Curt Maggi
D
Daniel Whitenack
朋友
Topics
Chris Benson 和 Daniel Whitenack 探讨了如何构建可靠的 AI 应用,特别关注从原型到生产环境的过渡。他们强调了测试的重要性,指出许多 AI 应用在 80% 的时间内运行良好,但在其余 20% 的时间内却会失败。他们建议将 AI 工作流分解成可测试的代码单元,并使用行为测试方法,包括最小功能测试、不变性测试和变异性测试,来评估模型的可靠性和对输入变化的敏感性。他们还讨论了低代码/无代码工具在 AI 工作流构建中的作用,以及这些工具的局限性。他们认为,虽然这些工具适合快速原型设计,但要实现生产环境的可靠性,仍然需要将工作流转化为可测试的代码。Curt Maggi 介绍了 Fly.io 平台,这是一个允许开发者快速构建和部署应用程序的平台。朋友介绍了 TimescaleDB,这是一个用于构建 AI 应用的 PostgreSQL 数据库。 Daniel Whitenack 指出,AI 代理的采用速度比预期慢,并且大型语言模型的发布速度也放缓。他认为,即使当前一代模型成为最佳模型,AI 仍然可以在企业和个人生活中产生显著的变革性影响。他强调了对现有工作流程进行评估和优化的重要性,以充分利用现有 AI 工具。 Chris Benson 认为,即使大型语言模型不再有新的突破,开源模型的兴起也会使商业模型的价值降低,并加速 AI 的普及。他指出,当前的大型语言模型可以作为工具的编排层,与其他专用工具结合使用,实现更复杂的 AI 应用。他认为,当前可用的 AI 工具已经足够强大,可以对文化和社会产生变革性影响。 朋友介绍了 TimescaleDB,这是一个用于构建 AI 应用的 PostgreSQL 数据库,它允许开发者使用熟悉的 PostgreSQL 和 SQL 语言来构建 AI 应用,无需学习新的技术。

Deep Dive

Chapters
The episode discusses the challenges of creating reliable AI applications, focusing on the transition from prototype to production and the importance of behavior testing.
  • AI applications often work well 80% of the time but fail 20% of the time.
  • Behavior testing and the flow from prototype to production are crucial.
  • The release of frontier models has slowed down, affecting expectations.

Shownotes Transcript

It can be frustrating to get an AI application working amazingly well 80% of the time and failing miserably the other 20%. How can you close the gap and create something that you rely on? Chris and Daniel talk through this process, behavior testing, and the flow from prototype to production in this episode. They also talk a bit about the apparent slow down in the release of frontier models.

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Something missing or broken? PRs welcome!)