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1
Introduction
2
Fine-Tuning Workflow Overview
3
Developing the Dataset
4
Data Ingestion Process
5
Evaluation Setup
6
Quality Model Development
7
Annotation Process
8
Fine-Tuning Job Initiation
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Evaluating Fine-Tuned Model
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Conclusion
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn how to enhance AI model performance through an 11-minute technical demonstration that showcases the integration of AWS and Snorkel Flow for fine-tuning AI co-pilots in healthcare document analysis. Follow along as a Machine Learning Solutions Engineer guides you through a structured workflow, from data ingestion to model evaluation, specifically focused on analyzing health insurance policy documents. Master the process of developing quality models that classify question-answer pairs, create effective labeling schemas, and fine-tune baseline Llama models for improved response accuracy. Gain practical insights into addressing common challenges such as informal language and incomplete responses, while learning to measure and compare acceptance rates before and after fine-tuning. Through detailed steps covering dataset development, evaluation setup, annotation processes, and fine-tuning job initiation, discover how to leverage AWS and Snorkel Flow to create more reliable AI solutions for healthcare applications. Read more

Fine-tuning AI Models for Healthcare Policy Analysis with AWS and Snorkel Flow

Snorkel AI
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