Identifying and clearly defining the problem or task that the AI model will address. This step is crucial for understanding the goals and objectives of the model.

Gathering relevant and representative data that will be used to train, validate, and test the AI model. The quality and diversity of the data significantly impact the model's performance.

Cleaning and transforming the raw data to prepare it for training. This includes handling missing values, outliers, and formatting the data into a suitable structure.

Choosing the appropriate algorithm or model architecture based on the nature of the problem. Common types include linear regression, decision trees, neural networks, etc.

Adjusting the model's parameters using the training dataset to minimize the difference between predicted and actual outcomes. This step is iterative and involves optimizing the model for performance.

Integrating the trained model into a production environment, making it accessible for real-world use. This often involves creating APIs or embedding the model into applications.

Integrating the trained model into a production environment, making it accessible for real-world use. This often involves creating APIs or embedding the model into applications.

Continuously maintaining the model's performance in the production environment, updating it as needed, and addressing any issues that may arise.