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Creating tested, reliable AI applications (Practical AI #295)
Nov 13, 2024
Kurt Mackey, Co-founder and CEO of Fly.io, dives into the challenges of developing reliable AI applications. He discusses the gap between project prototypes and production-ready models, emphasizing the importance of structured testing. The conversation also touches on the evolving landscape of AI technologies, the impact of open-source versus proprietary models, and the necessity of robust workflows. Mackey highlights trends in AI deployment and the significance of a reliable testing framework to ensure AI applications perform consistently.
50:09
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Quick takeaways
- Developers can enhance AI application reliability through structured testing and modular coding practices that align with traditional software engineering.
- The slowing release of advanced AI models highlights the importance of exploring open-source alternatives and continuous learning in AI development.
Deep dives
Explaining the Fly Development Platform
Fly is positioned as a flexible platform tailored for developers, allowing them to build and ship applications more efficiently. Unlike traditional platforms like Heroku or Vercel, which can impose restrictive limits, Fly offers a no-limits environment where developers can run applications close to their user base globally. It addresses common frustrations, such as the inability to perform advanced functionalities without hitting a wall, by providing foundational tools and capabilities enabling deeper integration and creativity. By emphasizing its ease of use—'an app in five minutes' while suggesting endless possibilities for complex development—Fly differentiates itself with the promise of ongoing developer empowerment.
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