Fine-Tuning on Amazon Bedrock: The Cheat Sheet for AWS Exams (Lesson 6)

Preparing for the AWS AI Practitioner certification?
This video explains fine-tuning foundation models in Amazon Bedrock one of the most important concepts you need to understand for both the exam and real-world GenAI projects on AWS.

You’ll learn what fine-tuning is, why it matters, and how it works in Amazon Bedrock, including the two fine-tuning approaches AWS expects you to know:
Instruction-based fine-tuning (labeled prompt-response data)
Continued pre-training (large volumes of unlabeled domain text)

We also cover exam-critical topics like:
How fine-tuning changes model weights (not just examples)
Why provisioned throughput is required to run fine-tuned models
Cost implications you must understand for exam questions
How fine-tuning fits under the broader concept of transfer learning

To help you lock this in, the video ends with a clear exam cheat sheet so you know exactly how to identify the right answer on test day.

👉 Ideal for:

AWS AI Practitioner exam preparation
Learning Amazon Bedrock fine-tuning
Understanding foundation models on AWS
Anyone building custom GenAI applications

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Timestamp
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[00:15] Lesson Agenda: Customization, Costs, and Cheat Sheets
[00:36] Why Customize? The Limits of General Foundation Models
[01:07] What is Fine-Tuning? Changing the "Internal Wiring"
[01:35] The Bedrock Fine-Tuning Workflow (S3 to Specialized Model)
[01:55] EXAM ALERT: Provisioned Throughput & Cost Implications
[02:22] Instruction-Based Fine-Tuning (Labeled Data)
[02:44] Continued Pre-Training (Unlabeled Data / Domain Expertise)
[03:30] The Practical Side: Effort, ML Engineers, and Pricing
[04:28] Transfer Learning vs. Fine-Tuning: The Relationship
[05:18] Final Cheat Sheet: 5 Must-Know Points for the Exam

Find me here
LinkedIn - https://www.linkedin.com/in/girish-mukim/
Website - https://imaginetechverse.com/
Twitter - https://twitter.com/GirishMukim
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Видео Fine-Tuning on Amazon Bedrock: The Cheat Sheet for AWS Exams (Lesson 6) канала AWSLearn (by Girish Mukim)