How AI models are developed and the training process

What is the primary difference between an AI tool and an AI model?

• AI tools are the applications, and AI models are the underlying programs.

• AI tools are the brain, and AI models are the applications.

• AI tools are for creating, and AI models are for searching.

• AI tools are static, and AI models are continuously improved.

What is the very first step in developing an AI model?

• Collecting relevant data.

• Training the model with prepared data.

• Clearly defining the problem the AI model needs to solve.

• Evaluating the model's performance.

When collecting data for an AI model, what is generally true about the amount of data?

• Less data is better for faster training.

• The amount of data does not significantly impact the model's performance.

• More data generally leads to better model performance.

• Only perfectly clean data is useful, regardless of quantity.

Why is it necessary to prepare data before feeding it into an AI model?

• To make the data more visually appealing.

• To ensure the data is neat, organized, and properly labeled for the model to learn from.

• To reduce the total amount of data.

• To encrypt the data for security purposes.

What happens to AI models after they are deployed for use?

• They become static and no longer require attention.

• They are continuously monitored and improved based on new data or performance drops.

• They are immediately replaced by new models.

• They only require re-training if a major error occurs.