Implementation of artificial intelligence (AI) solutions follows an organized process that typically involves several stages. Below is a structured description of how AI solution implementation typically works:
- Problem Definition: Identify the specific problem and establish goals and success criteria for the AI solution.
- Data Collection and Preprocessing: Gather relevant data and ensure its quality through cleaning, normalization, and transformation for preparation in model training.
- AI Model Selection: Choose the appropriate model architecture, such as neural networks or decision trees, and configure its hyperparameters.
- Model Training: Split the data into training, validation, and test sets. Iterate on training by adjusting parameters to improve model performance.
- Model Validation and Deployment: Evaluate the model with validation and test data. Integrate the model into the production environment and configure the necessary infrastructure.
- Monitoring and Maintenance: Implement a monitoring system to assess model performance in production and perform updates and periodic maintenance as needed.