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

All of the stages you listed are part of the generative AI model lifecycle:

• Idea Generation and Planning: This involves defining the problem you want the generative AI model to address and setting goals for its development.
• Data Collection and Preprocessing: Here, you collect relevant data to train the model and clean and prepare it for the training process.
• Model Architecture and Training: You choose an appropriate model architecture and train the model on the prepared data.
• Evaluation and Benchmarking: You evaluate the model’s performance on unseen data and compare it to other models or benchmarks.
• Model Deployment: If the model meets your criteria, you deploy it into production for real-world use.
• Content Generation and Delivery: The trained model generates content based on user input or prompts.
• Continuous Improvement: You monitor the model’s performance in production and collect feedback to further improve it through techniques like fine-tuning or retraining.

These stages form a cyclical process where the model’s performance in production informs future iterations and improvements.