Which of the following stages are part of the generative AI model lifecycle?

All of the following stages are part of the generative AI model lifecycle:

• Idea Generation and Planning: This involves defining the problem or opportunity you want the generative AI to address.
• Data Collection and Preprocessing: Here, you gather and clean the data the model will be trained on.
• Model Architecture and Training: You choose an appropriate model architecture and train it on the prepared data.
• Evaluation and Benchmarking: You assess the model's performance and compare it to other models or benchmarks.
• Model Deployment: If the model meets your criteria, you deploy it for real-world use.
• Content Generation and Delivery: The trained model generates content based on user prompts or instructions.
• Continuous Improvement: You monitor the model's performance and retrain it with new data or adjust its parameters as needed.

The generative AI model lifecycle is an iterative process. As you learn more from the deployed model, you can go back and refine any of the previous stages.