Testing AI Models
Daniel explores the challenges and considerations of testing AI models, particularly in the context of integrating them into workflows. He draws parallels between the current confusion around AI and past discussions in data science, emphasizing that non-deterministic models can still be effectively tested. With insights from his physics background, he encourages a mindset shift to embrace the probabilistic nature of AI technologies.In this clip
From this podcast
Practical AI
Creating tested, reliable AI applications
Related Questions
I have a question about the episode Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 and the clip Evaluating AI Reasoning. Have you seen a way to unit test large language models (LLMs) that are super helpful, as discussed in the episode How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269?
What problems do developers face when building AI applications, as discussed in the episode Creating tested, reliable AI applications and the clip Evolving Development Workflows?
How will large language models (LLMs) and AI change software engineering and the software development lifecycle (SDLC) as discussed in the episode Shreya Rajpal: Guardrails AI, AI Production Challenges, & AI Reliability | Around the Prompt #9 and the clip AI Validation Insights?