Machine learning models are dynamic predictors that are influenced by data, hyperparameters, evaluation metrics, and a variety of other factors; understanding the training and deployment process is critical to avoiding model drift predictive stasis. Not all monitoring solutions, however, are made equal. These are the three features that every machine learning monitoring tool should have, regardless of whether you design or buy one.
Complete Process Visibility
Multiple models are used in many applications, and these models serve a higher business goal that may be two or three levels downstream. Furthermore, the model’s behavior will almost certainly be influenced by data modifications that are performed several stages ahead. As a result, a basic monitoring system that focuses on single model activity will miss the big picture of model performance in the context of the entire business. Complete process visibility – having access to the full data flow, metadata, context, and overarching business processes on which the modeling is built – provides more profound knowledge of model viability. A bank, for example, may use a set of models to analyze creditworthiness, check for suspected fraud, and dynamically allocate trending offers and promos as part of a credit approval application. A simple monitoring system might be able to analyze each of these models separately, but understanding the interplay between them is required to solve the overall business problem. While their modeling goals may differ, each model is built on a common base of training data, context, and business metadata. As a result, a good monitoring system will take these diverse elements into consideration and produce unified insights that leverage this common data. Identifying specialized and underutilized consumer segments in the training data distribution, reporting probable instances of concept and data drift, understanding the aggregate model influence on business KPIs, and more are examples of these tasks. The finest monitoring solutions can operate with conventional, tabularized data as well as ML models, allowing the monitoring solution to be extended to all business use-cases, not just those using ML.
Proactive, Intelligent Insights
Any monitoring solution will help you understand how your model is acting on the surface, but in most circumstances, that’s not enough. A typical misunderstanding is that a monitoring solution should serve as a visualization tool, exhibiting the common metrics connected with an ML model during the training and deployment process. While this is beneficial, metrics by themselves are useless unless they are used to inform decision-making. As a result, rather than numbers, insights based on those metrics are required. You might be able to see your customer data using a platform like Tableau. Even so, a genuinely successful monitoring solution will be able to segment that visualization and identify aberrant, non-performing, or outliers in some way. Any data type, metric, or model feature offered by an accurate monitoring tool will automatically supply this type of decision-making information.
The definition of “automatically” requires further clarification. Some monitoring tools have dashboards that let you manually explore subsets of data to identify what’s working and what isn’t. However, this simple introspection necessitates time-consuming manual intervention and overlooks the more important topic. A genuine monitoring solution will be able to discover anomalies internally through its mechanisms, rather than relying on an individual to supply their own hypothesis.
Finally, a good monitoring tool will help with noise reduction, possibly by detecting single anomalies that spread and cause problems in several locations. The monitoring tool is successful because it identifies the fundamental causes of problems rather than identifying surface-level data anomalies or the like.
Total Configurability
Finally, a genuine monitoring solution will be able to adapt to any scenario you can imagine. It should be able to take any model metric, any unstructured log, and, basically, any piece of tabular data and turn it into visuals, process insights, and actionable suggestions. This is due to the fact that different models have distinct requirements, and a general, one-size-fits-all solution will not be effective in all circumstances. When given data, a product recommendation system, for example, might be able to begin efficiently offering new products to users straight away. An optimal monitoring system for such a model would likely focus on preventing model drift from the start, therefore metrics that assess model drift would be high on the priority list. A fraud detection system, on the other hand, may require a first-pass deployment, during which it learns from tens or hundreds of thousands of real-world transactions before attempting to identify a ground truth. As it encounters new types of fraud and recalibrates, some model drift is expected and even desirable for this type of model. As a result, a monitoring solution that prioritizes insights into abnormal parts of the input data distribution would be more appropriate for this use case. The best monitoring solutions will be completely adjustable, allowing them to be tailored to the specific needs of the job.
Conclusion
Many solutions will take an ML model and provide shallow insights into its feature behavior, output distributions, and basic performance measures, given the ever-increasing buzz around machine learning. Complete process visibility, proactive, intelligent insights, and total configurability, on the other hand, are more uncommon. These three characteristics, however, are critical for extracting the best performance and downstream business impact from ML models. As a result, any monitoring solution must be evaluated through the lens of these three must-haves to ensure that it delivers model visibility as well as more global and complete awareness of the business context.
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