The text discusses the importance of testing and monitoring machine learning (ML) pipelines to prevent catastrophic failures. It emphasizes unit testing feature generation and cleaning, black box testing of the entire pipeline, and thorough validation of real data. The article also highlights the need for vigilance in monitoring predictions and features to ensure model relevance and accuracy.
How to Avoid Disaster
You may have a well-performing algorithm in testing, but deploying it into production can lead to unexpected challenges. Here are practical ways to ensure the smooth transition and ongoing success of your AI solutions:
Unit Test Around the Model
To ensure the reliability of your predictions, focus on unit testing feature generation and post-processing. Thoroughly test cleaning and bounds checking to maintain the integrity of your model’s inputs.
Consider using approximate asserts to avoid failures resulting from asserting on precise floating points.
Suspect the Integration
Create a suite of black box test cases to test the outputs of your entire pipeline. This helps detect unforeseen impacts quickly and ensures the robustness of your code.
Trust Has a Cost
Adopt a proactive approach by checking for known input ranges early in your pipeline. Implement noisy validation functions to catch any unexpected issues and protect your team from unfortunate consequences.
Run the Real Data Obstacle Course
Execute your ML pipeline against as much real data as possible to identify outliers, errors, and edge cases. This also facilitates performance testing and infrastructure cost estimation.
Consider a “soft launch” to spot any negative user feedback and real-world failures at a small scale before a general launch.
Testing is Never Enough
Beyond unit testing, implement an exception handling strategy, monitoring, and alerting to ensure the ongoing success of your AI solutions.
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