After building and selecting a predictive model, the next step is to deploy it into production and continuously monitor its performance. Here are some key aspects to consider:
Model Deployment Strategies: There are multiple ways to deploy predictive models, including integrating them into existing systems, creating APIs for real-time predictions, or building standalone applications.
Model Maintenance: Predictive models need regular maintenance to ensure they stay accurate and up to date. This may involve retraining the model periodically, adding new data, or updating the model with new features.
Continuous Monitoring: Once deployed, it is important to monitor the model's performance over time. This involves tracking prediction accuracy, detecting concept drift, and identifying any unexpected changes in data patterns.
By deploying and monitoring predictive models effectively, we can ensure that our predictions remain accurate and valuable in real-world scenarios.