AWS Certified AI Practitioner(4)

📚 Amazon Bedrock Fine-Tuning & Model Selection

---

1. Different Providers & Model Capabilities

• Providers: Anthropic, Amazon, DeepSeek, Stability AI, etc.
• Models vary in strengths:
  • Claude 3.5 Haiku → Best for text tasks.
  • Amazon Nova Reel → Text-to-video / Image-to-video.
• Exam Tip: You will not be tested on which is best, only on what each can or cannot do.

---

2. Comparing Models

• Compare Mode: Test models side-by-side in Bedrock playground.
• Compare by:
  • ✅ Capabilities (text, image, video)
  • ✅ Output style/format
  • ✅ Speed (latency)
  • ✅ Cost (token usage)
• Example:
  • Nova Micro: ❌ No image upload, faster, shorter responses.
  • Claude 3.5 Sonnet: ✅ Image support, longer/more detailed answers.

---

3. Fine-Tuning Methods – Comparison Table

Feature	Instruction-Based Fine-Tuning	Continued Pre-Training	Transfer Learning
Data Type	Labeled (prompt–response pairs)	Unlabeled (raw text)	Labeled or Unlabeled
Goal	Improve performance on domain-specific tasks	Make model expert in a specific domain	Adapt a pre-trained model to a new but related task
Example	Train chatbot to respond in a specific tone	Feed all AWS docs to become AWS expert	Adapt GPT for medical text classification
Changes Model Weights?	✅ Yes	✅ Yes	✅ Yes
Complexity	Medium	High	Varies
Cost	Lower (less data)	Higher (more data)	Varies
Exam Keyword	“Labeled data”, “prompt-response”	“Unlabeled data”, “domain adaptation”	“Adapt model to new task”
Bedrock Support	Supported on some models	Supported on some models	General ML concept (not Bedrock-specific)

Instruction-based Fine Tuning

Continued Pre-training

---

4. Messaging Fine-Tuning

• Single-Turn Messaging:
  • One Q → One A
  • Optional system context

• Multi-Turn Messaging:
  • Full conversations, alternating user and assistant
  • Used for chatbot training in multi-step dialog

---

5. Transfer Learning

• Definition: Using a pre-trained model for a new but related task.
• Common in:
  • Image classification
  • NLP (BERT, GPT)
• Exam Tip: If question is general ML → choose Transfer Learning as the answer.
  If it’s about Bedrock & domain-specific → choose Fine-Tuning.

---

6. Fine-Tuning Requirements in Amazon Bedrock

• Training data must:
  • Be in Amazon S3
  • Follow specific formatting

• Provisioned Throughput is required for:
  • Creating the custom model
  • Using the custom model

• Not all models can be fine-tuned (usually open-source models are supported).

---

7. Common Use Cases

• Chatbot with specific persona, tone, or target audience
• Adding up-to-date knowledge
• Integrating exclusive private data (customer logs, internal documents)
• Improving categorization, accuracy, or response style

---

8. Exam Tips

• Keyword Mapping:
  • “Labeled data” → Instruction-based Fine-Tuning
  • “Unlabeled data” / “Domain adaptation” → Continued Pre-Training
  • “Adapt to a new related task” → Transfer Learning
• Provisioned Throughput is a must for custom models in Bedrock.
• Fine-tuning changes weights of the base model → creates a private version.
• Compare models not just on quality, but also latency and token cost.

9. Good to know

• Re-training an FM requires a higher budget
• Instruction-based fine-tuning is usually cheaper as computations are less intense and the amount of data required usually less
• It also requires experienced ML engineers to perform the task
• You must prepare the data, do the fine-tuning, evaluate the model
• Running a fine-tuned model is also more expensive (provisioned throughput)

10. Provisioned Throughput (AWS Bedrock)

Definition:
Reserving a fixed amount of processing capacity for your custom (fine-tuned) model.

Why Needed

• Fine-tuned models run on dedicated resources.
• Ensures consistent speed, low latency, and predictable costs.
• Prevents slowdowns during high demand.

With vs Without

• Without: Performance can drop when traffic spikes.
• With: Guaranteed performance for the reserved capacity.

Exam Tip: For custom models in Bedrock, provisioned throughput is required.

---