What considerations should be taken into account during the deployment and maintenance phases of a data science project?
Deploying a model into a production environment requires careful consideration of factors such as scalability, security, and integration with existing systems. Once deployed, ongoing maintenance is essential to address changes in data patterns, monitor model performance, and update the model as needed. Continuous monitoring and feedback loops are critical for ensuring the long-term success of a data science project.