CertiPass LogoCertiPass

Improving AI Model Fine-Tuning with Amazon SageMaker JumpStart

BEGINNER
150 minutes
5 tasks

Use Amazon SageMaker JumpStart to explore fine-tuning pre-trained models. This lab will guide you through selecting, tuning, and evaluating AI models to achieve optimal performance.

Scenario

A health tech startup needs to customize an AI model to increase its efficacy in predicting patient outcomes. This lab allows you to practice fine-tuning using SageMaker JumpStart, focusing on improving model accuracy and customization.

Learning Objectives

  • Understand fine-tuning concepts in AI modeling
  • Explore SageMaker JumpStart to access pre-trained models
  • Adjust and evaluate model configurations for specific datasets

tasks (5)

task 1: Explore Pre-trained Models Using SageMaker JumpStart

20 min

task 2: Custom Fine-Tuning with Specific Healthcare Data

30 min

task 3: Evaluate and Validate Fine-Tuned Model Performance

30 min

task 4: Design a Deployment Plan for Real-world Application

40 min

task 5: Present a Comprehensive Report on the Tuning and Deployment Process

30 min

Prerequisites

  • •Familiarity with model tuning concepts
  • •Understanding of healthcare data applications

Skills Tested

Utilizing pre-trained models in AI applicationsApplying fine-tuning techniques to enhance model performanceEvaluating and reporting on AI model performance enhancements

References