Changelog Master Feed 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.
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AI Model Capabilities
- Current AI models are sufficient for orchestration, not specific tasks like time series forecasting.
- They can integrate with specialized tools, creating flexible workflows and automations.
AI Workflow Challenges
- Daniel Whitenack recounts teaching workshops and encountering a common issue: AI workflows that work well only half the time.
- He draws a parallel between this and the past struggles of using Jupyter notebooks in production due to their ad-hoc nature.
Productionizing AI Workflows
- Treat AI workflow components like regular software functions, emphasizing proper testing.
- Decouple workflows from low-code tools for scalability and reliability.