Tomaz Kastrun wraps up a series on Azure AI. First up is tracing in Azure AI Foundry:
Tracing is a powerful tool that offers developers an in-depth understanding of the execution process of their generative AI applications. Though still in preview (in the time of writing this post), It provides a detailed view of the execution flow of the application and the essential information for debugging or optimisations.
After that, we can see how to evaluate model results:
With evaluation you performing iterative, systematic evaluations with the right evaluators and measure and address potential response quality, safety, or security concerns throughout the AI development lifecycle, from initial model selection through post-production monitoring.
With the Evaluation in Azure AI Foundry, you can evaluation the GenAI Ops Lifecycle production. In addition, it also gives you the ability to assess the frequency and severity of content risks or undesirable behavior in AI responses.
Finally, Tomaz wraps up the series with some notes on documentation:
Documentation and material for Azure AI Foundry are plentiful and growing on a daily basis, since the topic on AI and GenAI is evermore so popular.
I appreciate the challenge that Tomaz has of putting together 25 blog posts in a month, especially when they’re all tied to a single theme.