SageMaker JumpStart provides a low-code/no-code (LCNC) solution for fine-tuning pre-trained foundation models, including LLMs for tasks like text summarization. It streamlines the process by offering a graphical user interface within SageMaker Studio to select a model, provide a training dataset, and launch a fine-tuning job. This approach automates the complex setup of training infrastructure and minimizes the need for manual coding or intervention, directly aligning with the team's requirements for an automated LCNC solution.

A. SageMaker Training Jobs are a powerful tool but require users to write and provide their own training scripts and container configurations, which is not a low-code/no-code approach.

B. Fine-tuning on raw Amazon EC2 instances, even when launched from SageMaker Studio, necessitates manual environment setup, library management, and coding, contradicting the LCNC requirement.

C. SageMaker Script Mode is specifically designed for users to bring their own custom training scripts, making it a code-intensive method that offers flexibility at the cost of simplicity.

1. Amazon SageMaker Developer Guide: Under the "Amazon SageMaker JumpStart" section, it states, "You can fine-tune models with no code through SageMaker JumpStart's user interface in Studio." This confirms that JumpStart is the designated LCNC solution for this task. (See the subsection: "Fine-tune a model with SageMaker JumpStart").

2. Amazon SageMaker Developer Guide: The section "Create a training job with the Amazon SageMaker Python SDK" describes how to use SageMaker Training Jobs. The process involves creating an estimator object and providing an entry-point script, demonstrating it is a code-first approach.

3. Amazon SageMaker Developer Guide: The "Use Script Mode" page details how to package custom Python code for use with SageMaker, highlighting that it is fundamentally a code-based solution for users who need custom logic.

4. Amazon SageMaker Developer Guide: The "Automate model development with Amazon SageMaker Autopilot" section describes Autopilot's function for tabular data (classification and regression), not for fine-tuning LLMs. However, the combination with JumpStart in the option points to the automated, UI-driven workflow that JumpStart provides for foundation models.