What are the 4 components of AI deployment?

The 4 main components of AI deployment are:

1. Data Preparation and Model Training: This stage involves ensuring your data is high quality and formatted correctly for the chosen AI model. You then train the model on this data, allowing it to learn the patterns and relationships necessary to perform its task.

2. Model Selection and Optimization: After training various models (if applicable), you select the one that delivers the best performance on your specific problem. This might involve fine-tuning the chosen model to further improve its accuracy or efficiency for your use case.

3. Model Deployment and Infrastructure: Here, you deploy the trained model into a production environment where it can be used to make real-world predictions or generate content. This involves choosing the appropriate computing infrastructure (cloud, on-premise servers, etc.) that can handle the model’s processing needs.

4. Monitoring and Maintenance: Once deployed, it’s crucial to monitor the model’s performance in production. This includes tracking metrics like accuracy, bias, and drift (performance degradation over time). Based on this monitoring, you can perform maintenance tasks like retraining the model with new data or adjusting its parameters to ensure it continues to deliver optimal results.