Choose a base foundation model Clean and prepare the training dataset Fine-tune the model on the curated data Validate the tuned model's performance Release the tuned model to production is correct. This sequence follows the standard machine learning lifecycle so you select the most appropriate base model first and then prepare data that matches that model so training can proceed effectively.

Choose a base foundation model Clean and prepare the training dataset Fine-tune the model on the curated data Validate the tuned model's performance Release the tuned model to production is correct because choosing the base model first informs the data format and preprocessing steps and then fine tuning uses the curated dataset to adapt the model to organization specific content. Validation comes after training so you can measure performance against held out data and then deployment follows successful evaluation.

Clean and prepare the training dataset Choose a base foundation model Fine-tune the model on the curated data Validate the tuned model's performance Release the tuned model to production is incorrect because preparing data before choosing a base model can lead to mismatches in expected input formats and feature requirements for the selected model.

Choose a base foundation model Fine-tune the model on the curated data Validate the tuned model's performance Clean and prepare the training dataset Release the tuned model to production is incorrect because attempting to fine tune before cleaning and preparing the data is not feasible and will produce unreliable training outcomes.

Choose a base foundation model Clean and prepare the training dataset Validate the tuned model's performance Fine-tune the model on the curated data Release the tuned model to production is incorrect because validation before any training means you have nothing trained to evaluate and the assessment becomes invalid.

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