Data scientists identify model failure when revenue ceases to flow in, response metrics decline or any of the tracked key metrics plummet. When conducting a postmortem on a failed ML model, uncovering certain characteristics and commonalities across all failures is inevitable. In this article, we’ll outline steps for performing a postmortem analysis on an ML model failure.
In simpler terms, if the model is no longer useful or has become irrelevant in the ecosystem.
All of these outcomes are undesirable when a model is deployed in production and fails to meet expectations.
Reviewing the aforementioned statements, one can observe that they all raise concerns about the model’s performance quality. Initially, quality is a mere perception or reflection of user satisfaction.
However, to genuinely assess whether a model’s deterioration is sufficient to declare it deceased, quantifying changes in model performance is essential. Shifting from perceived indications of quality to observed and measured metrics that reveal the extent of performance decline is necessary.
Upon reviewing the highlighted portions in the above statements, such as consistently and in most cases, it becomes evident that they reflect perceived deterioration.
Instead, incorporating specific numbers and ratios, like a 45% failure rate or one-third of cases, offers a more insightful perspective on performance changes.
This approach enables us to analyze shifts in model performance concerning accuracy metrics like precision and recall.
Once the model performance is quantified, the ideal approach involves comparing it against metrics from the training environment.
This methodology aids in identifying scenarios such as:
Each scenario necessitates a distinct approach.
Thus far, we have explored how to troubleshoot problematic models in production and address observed issues.
To offer a fair evaluation of a model and pinpoint its failure, comprehending the disparity between model training and the production environment is crucial.
As depicted in the image above, the model training environment encompasses the following:
The training process typically concludes by selecting the best model version based on observed performance metrics. Once the suitable model version is determined, it is deployed in the production environment.
For instance, consider a model forecasting weekly sales of certain product types:
These changes significantly alter the operational conditions under which the deployed model functions.
Consequently, the model is likely to deteriorate and perform poorly in such scenarios.
Therefore, these situations demand a proper troubleshooting approach instead of making a sweeping statement like, “Oh, the model is dead, it’s useless!”
The following image summarizes potential troubleshooting steps to rectify a model perceived as failed.
The following set of questions aids in addressing issues related to model performance and failures:
If the underlying data lacks quality, all other troubleshooting attempts regarding model training, deployment, etc., become irrelevant. Regardless of how sophisticated the model may be, if the data used is of poor quality, the resulting model outputs will reflect the same.
Ensuring high-quality input data is crucial for obtaining meaningful model outcomes. Data quality encompasses metrics such as data completeness, standardization, consistency, and accuracy. Analyzing these metrics can help correlate model failure with data quality.
Sometimes, addressing a model’s failure could involve optimizing the runtime performance of the deployed scoring function. For instance, if users experience prolonged wait times for model predictions, it could test their patience and impact user satisfaction.
Monitoring the latency of real-time predictions is vital for machine learning models deployed in such scenarios.
Additionally, if models operate in batch mode and predictions are delayed, the decay in prediction accuracy could result in response metric degradation. Monitoring scoring throughput is essential in these batch mode scenarios.
Understanding factors contributing to changes in model accuracy against the training dataset is crucial when accuracy significantly drops. Such changes often indicate the need for model retraining and hyperparameter tuning. If accuracy remains high and prediction drift is minimal, but response metrics suffer, the focus should shift to the channels connecting predictions to consumers. Potential issues might arise from prediction delivery channels, message decay, or message content.
While data quality metrics serve as high-level indicators for detecting ML model failures, the true indicators, if measurable, lie in data drift related to features or underlying data. Monitoring data attribute drift involves tracking statistical attributes of the same data points in a time series. Analyzing data drift of crucial attributes for an ML model can swiftly reveal the causes of model failure.
Understanding the reasons behind an ML model’s failure can lead us down various paths, each of which may require substantial effort and time. Exploring available tools that shed light on mysterious cases of ML model failure reveals promising options for monitoring model decay, features, responses, and data quality. Qualdo™ seamlessly integrates Data Reliability with the essential forensics for comprehending ML model monitoring and decay.
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