Hidden Technical Debt of Machine Learning – Play Now Pay Later

Last week I was lucky enough to attend the Strata Conference London 2017 for one day. The venue and the event are impressive in scale, participants and content. The quality of tutorials and talks, in general, was very good, and I have walked away with a few new ideas I wanted to share on my blog.

One of the most important lessons from the conference for me was from a reference to the NIPS’16 paper titled Hidden Technical Debt in Machine Learning System, written by Google researchers. The paper is about the long-term maintenance costs introduced by building machine learning (ML) models and systems. The argument is that such cost is hidden as it is not immediately apparent from the point of putting an ML model in production. For data scientists it is important to be aware of the complexity of the models they develop and what impact these models will have on their organisation and how much it will cost to maintain them.

According to the authors, there are three levels of technical complexity which contribute to technical debt in ML: the model itself can be complex and behave non-linearly to a given set of parameters, the model can be taking input from otherwise disparate systems, and the model’s output or its behavior can be complex and difficult to predict before it is released.

ML Model Complexity

ML models entangle input signals from different systems together, making it difficult to avoid the CACE principle: Change Anything Change Everything. This principle applies to all aspects of ML, from parameters (think xgboost!), to input data to convergence thresholds and sampling methods. Isolation and servicing of modelling components is one of the proposed solutions.

The Cost  of Data Dependencies

Large ML systems have large and complex data dependencies, where data quality and any data assumptions can significantly affect the ML system output. ML system input data can be unstable, meaning it changes qualitatively and quantitatively over time. In some cases, the degree of dependency on one set of data vs. another may change. The ML systems are unique because usually their data dependencies are finer (e.g. the input should not just be an integer, but an integer in a certain range). A lot of thinking and possibly investment should go into understanding such dependencies and controlling them. Check-out kensu.io – a start-up company I have come across at the conference, the creators of Adalog – a product designed purely for such task.

The Feedback Loop and Dealing with Changes

Live ML systems learn in real time and influence their own behavior. Sometimes it is necessary to choose static parameters, like prediction thresholds, for a model that is trained or parameterised on  data that is dynamic in nature. Thus leading to the previous set of thresholds being no longer valid on updated data. The authors highlight that comprehensive monitoring of ML system behavior is critical for long-term system reliability.

In summary, maintainable ML systems are costly and require an even higher level of technical competence and foresight among its developers.  ML models testing, validation and monitoring should be considered as an absolute must in organisations that are eager to rip their full benefits.

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