As a data scientist, you're handed a project to predict future sales for a retail company. You've gathered the data, cleaned it, and built a predictive model. Before deploying this model, what step should you prioritize to ensure it will function as expected in a real-world setting?
- Fine-tuning the model
- Data preprocessing
- Model evaluation
- Monitoring the model's performance
Monitoring the model's performance is crucial to ensure that it functions as expected in a real-world setting. This involves continuous evaluation and making adjustments as needed to adapt to changing data and ensure the model remains accurate and reliable over time.
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