How do you compare machine learning models in deployment?

1 Model performance metrics

One way to compare machine learning models in deployment is to use metrics that measure how well they achieve their objectives. Depending on the type of model and the problem domain, you might use metrics such as accuracy, precision, recall, F1-score, ROC-AUC, MSE, MAE, or R-squared. These metrics can help you assess how accurate, reliable, and consistent your models are in making predictions or classifications. However, metrics alone are not enough to compare models in deployment. You also need to consider other factors, such as data quality, business impact, and user feedback.

2 Data quality and drift

Another way to compare machine learning models in deployment is to monitor the quality and distribution of the data they use and produce. Data quality refers to how complete, accurate, and consistent the data is. Data drift refers to how the data changes over time and deviates from the original assumptions or expectations. Both data quality and data drift can affect the performance and validity of your models in deployment. Therefore, you need to check for data quality issues, such as missing values, outliers, or errors, and data drift issues, such as concept drift, covariate shift, or distribution shift. You can use techniques such as data profiling, data validation, or data drift detection to identify and address these issues and compare how different models handle them.

3 Business impact and value

A third way to compare machine learning models in deployment is to measure their impact and value on the business goals and outcomes. This means going beyond the technical metrics and looking at how the models affect the key performance indicators (KPIs), return on investment (ROI), or customer satisfaction. For example, you might want to compare how different models increase revenue, reduce costs, improve efficiency, or enhance user experience. You can use techniques such as A/B testing, multi-armed bandit, or causal inference to estimate and compare the causal effects of different models on the business metrics and outcomes.

4 User feedback and behavior

A fourth way to compare machine learning models in deployment is to collect and analyze user feedback and behavior. User feedback refers to how the users perceive and evaluate the models, such as their satisfaction, trust, or preference. User behavior refers to how the users interact and respond to the models, such as their engagement, retention, or conversion. Both user feedback and behavior can provide valuable insights into how the models meet the user needs and expectations. You can use techniques such as surveys, ratings, reviews, or analytics to gather and compare user feedback and behavior for different models.

5 Best practices and challenges

Comparing machine learning models in deployment is not a simple task and requires a systematic and continuous process of monitoring, evaluating, and improving the models. When doing so, it is important to define clear and relevant comparison criteria and metrics that align with model objectives, problem domain, and stakeholder expectations. Additionally, data should be collected from multiple sources and perspectives, such as technical metrics, data quality, business impact, and user feedback. Experiments and controls should be used to isolate and estimate causal effects. Furthermore, results and insights should be communicated effectively with visualizations that highlight key findings and trends. Finally, models should be updated and iterated based on the results and feedback in order to adapt to changing data, environment, and user needs.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?