Bedrock Model Customization

Describe the purpose of the AWS Management Console as the web interface for managing cloud services.

Explain what Amazon Bedrock is and its primary function as a managed service for accessing foundation models.

Identify the role of Amazon S3 for storing data, specifically the training datasets required for model customization.

Explain the purpose of AWS Identity and Access Management (IAM) for managing permissions to services like Bedrock and S3.

Describe the required format for a fine-tuning dataset, using the concept of prompt-completion pairs.

Outline the conceptual process of uploading a prepared training dataset to an Amazon S3 bucket.

Explain the difference between training data and validation data in the context of fine-tuning.

Describe how to navigate to the model customization section within the Amazon Bedrock console.

Identify the key configuration settings required to start a fine-tuning job, such as selecting a base model and specifying data locations in S3.

Explain the high-level concept of hyperparameters (e.g., epochs, learning rate) in a fine-tuning job.

Describe how to initiate and monitor the status of a running fine-tuning job in Bedrock.

Explain how to access and test the resulting custom model after the fine-tuning job is successfully completed.