Creating tested, reliable AI applications

Testing Strategies

Testing AI workflows involves creating a structured approach to ensure each component functions correctly. Daniel Whitenack suggests breaking down workflows into discrete steps, each with its own tests, similar to traditional software engineering practices 1. This approach includes creating tables of deterministic outputs and unit tests to verify each function or class 2. Chris Benson agrees, noting that this method aligns with good data science practices 3.

You should have tasks for each of those kind of subtasks in the chain of processing.

--- Daniel Whitenack

This structured testing ensures that AI applications are reliable and consistent, even when integrated into complex workflows.

   

Model Sensitivity

Evaluating model sensitivity is crucial for understanding how AI models respond to changes in input. Daniel Whitenack emphasizes the importance of creating tables for minimum functionality, invariant, and variant tests to assess model behavior 4. These tests help identify how sensitive a model is to input changes, ensuring that it performs reliably across different scenarios.

This sensitivity is really the thing that people get hung up on with these workflows.

--- Daniel Whitenack

By systematically probing model sensitivity, developers can work towards improving AI systems' robustness and accuracy.

   

Navigating Failures

Navigating failures in AI workflows requires a strategic approach to transition from prototypes to production. Daniel Whitenack discusses the challenges of moving from low-code tools to scalable, tested code 5. He highlights the need for embedding workflow steps into functions or classes that can be systematically tested 6.

It does take actual work to go from that notebook state to the production code.

--- Daniel Whitenack

This process ensures that AI applications are not only functional but also reliable when deployed in real-world environments.