Timeless Challenges: Debugging a Migrated Machine Learning Algorithm (10+ Years Ago)
The world of data may evolve rapidly, but some challenges remain constant. Over a decade ago, I encountered one such challenge while migrating a machine learning algorithm from Oracle SQL to Microsoft SQL Server following a merger and acquisition (M&A).
Lost Knowledge and Data Precision Discrepancies
The M&A transition didn't include the original developers, creating a knowledge gap. This, coupled with the subtle differences in data type precision between Oracle SQL and SQL Server, led to unexpected discrepancies in the algorithm's output. Distinguishing between rounding errors due to data type differences and true computational errors within the algorithm itself proved difficult.
A Strategic Debugging Approach with Timeless Techniques
To tackle this challenge, I employed a two-pronged strategy:
Targeted Measurement Points: Drawing on principles of numerical analysis, I designed a software tool that strategically placed measurement points within the algorithm's workflow. This allowed us to monitor calculations at various stages and pinpoint potential deviations.
Logarithmic Difference Analysis: Beyond Rounding Errors: To differentiate between rounding errors and true computational errors, I analyzed the log10 of the absolute difference between the expected and observed values. While not everyone remembers pre-calculus off the top of their heads, the key benefit of this technique is that it allows you to directly express the impact of precision loss. We could clearly state at which stage we were losing a specific number of correct digits. This approach proved more user-friendly and universally applicable compared to relying on everyone's memory of advanced mathematical concepts.
Collaboration: The Key to Success
The success of this debugging effort stemmed from collaboration. We secured a pilot client who generously allowed us to test the algorithm against historical data. The database developer's expertise was invaluable. Running the algorithm on both platforms (Oracle SQL and SQL Server) and assisting with downloading test data sets were crucial steps in pinpointing the source of the discrepancies and ensuring the algorithm delivered accurate results.
Lessons Learned: Always Relevant
This project, while completed over a decade ago, serves as a reminder that the need for systematic debugging and collaboration in complex systems remains ever-present. Additionally, it highlighted the importance of user-friendly debugging techniques that are accessible to a broader audience, regardless of their specific mathematical background.