Keywords: Machine Learning, Production, Metrics, Model Explainability, Data Drift
Overview: This article discusses the challenges of transitioning machine learning models from development to production, focusing on the pre-release phase. It emphasizes the importance of aligning technical choices with business goals, optimizing loss functions and metrics, prioritizing representative metrics over inflated ones, and being mindful of model uncertainty. The article also highlights the significance of model explainability and preparing for data and label drift in production systems. The author uses the Kaggle Fraud Detection dataset as a case study to illustrate these concepts, advocating for an iterative approach to model development and deployment.
Sectional Reading:
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Optimizing Loss Functions and Metrics for Impact:
- It’s crucial to translate qualitative business goals into quantitative metrics to guide model building.
- While F1 score and BCE loss are good starting points, they may not always align with business priorities, especially in imbalanced datasets.
- Adjusting the loss function and evaluation metric, such as using F-beta, can lead to a model that better reflects business needs.
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Prioritize Representative Metrics Over Inflated Ones:
- Presenting metrics that accurately reflect the model’s performance is essential for building trust and setting realistic expectations.
- Splitting data to mirror real-world scenarios, such as using a temporal split, is crucial for obtaining representative metrics.
- Validation datasets provide a more honest assessment of model performance compared to test sets used for parameter optimization.
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Be Mindful of Model Uncertainty:
- Addressing model uncertainty can lead to smoother model deployment by focusing on high-confidence predictions.
- For classification tasks, implementing a threshold on the model’s output can ensure only confident labels are outputted.
- For regression tasks, presenting a confidence interval rather than a point estimate can better reflect the model’s uncertainty.
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Model Explainability:
- Model explainability is important for building trust in machine learning models and detecting overfitting.
- Analyzing feature importance can provide insights into the model’s behavior and help identify potential issues.
- Explainability can also reveal surprising insights that enhance subject matter expertise.
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Preparing for Data and Label Drift in Production Systems:
- Data and label distributions rarely remain stationary over time, and data drift is a natural phenomenon.
- Instead of resisting data drift, it should be incorporated into system design by monitoring and planning for retraining.
- Tools like Deepchecks can be used to analyze feature drift and help prepare for changes in data distributions.
Related Tools:
- TabNet: (Link to the author’s previous article: https://medium.com/towards-data-science/real-world-use-cases-forecasting-service-utilization-using-tabnet-and-optuna-26308db615c3)
- Optuna: (Link to the author’s previous article: https://medium.com/towards-data-science/real-world-use-cases-forecasting-service-utilization-using-tabnet-and-optuna-26308db615c3)
- Deepchecks: (No direct link provided in the article, but it’s a Python library for data and model validation)
References:
- Kaggle Fraud Detection dataset: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud/data
- Author’s previous article on TabNet and Optuna: https://medium.com/towards-data-science/real-world-use-cases-forecasting-service-utilization-using-tabnet-and-optuna-26308db615c3
- Author’s previous article on model uncertainty thresholding: https://medium.com/towards-data-science/mastering-model-uncertainty-thresholding-techniques-in-deep-learning-1f1ab3912fd1
Original Article Link: https://towardsdatascience.com/real-world-use-cases-strategies-that-will-bridge-the-gap-between-development-and-productionizing-87765f00c4c4