In data science, a deployed model refers to a machine learning model that has been integrated into a production environment or system and is actively used to make predictions or provide insights. It is the operational version of a trained model that is made available to end-users or other systems for real-time or batch processing.

Deployed models are crucial in various data science applications and domains, such as recommendation systems, fraud detection, image recognition, natural language processing, and predictive analytics. They enable organizations to leverage the power of machine learning and artificial intelligence to automate decision-making processes, improve efficiency, and gain valuable insights from data in real-world scenarios.  By obtaining Data Science with Python, you can advance your career in Data Science. With this course, you can demonstrate your expertise in data operations, file operations, various Python libraries, many more fundamental concepts, and many more critical concepts among others.

When a model is deployed, it means that it has undergone the necessary steps to be accessible and utilized in a production setting.

These steps typically involve:

1. Model Training and Evaluation: The model is developed using training data, where it learns patterns and relationships to make predictions or classifications. The model's performance is evaluated using evaluation metrics to ensure its accuracy and suitability for the intended task.

2. Model Serialization: The trained model is serialized or saved in a format that can be easily loaded and used in a production environment. This serialization process ensures that the model's parameters, structure, and associated metadata are preserved.

3. Integration into Production Systems: The deployed model is integrated into the production environment, which can involve various tasks such as setting up the necessary infrastructure, connecting to data sources, and ensuring compatibility with existing systems and frameworks.

4. Data Preprocessing and Input Handling: The deployed model may require specific data preprocessing steps to transform the input data into a suitable format for making predictions. This can involve scaling, encoding categorical variables, handling missing values, or applying feature engineering techniques.

5. Model Serving: The deployed model is made available through an API or other means to accept requests or data inputs for prediction or inference. It should handle input validation, perform the necessary transformations on the input data, and return predictions or insights in an appropriate format.

6. Monitoring and Maintenance: Once a model is deployed, it requires ongoing monitoring and maintenance to ensure its continued effectiveness. This includes monitoring performance metrics, tracking data drift, addressing model degradation over time, and periodically retraining or updating the model as new data becomes available.