• Contents:

FMs in Bedrock - Overview

Ref: https://www.udemy.com/course/aws-ai-practitioner-certified/learn/lecture/44886343

• Choosing an adequate FM depends on a lot of factors!
  • Model types, performance requirements, capabilities, constraints, compliance
  • Level of customization, model size, inference options, licensing agreements, context windows, latency
  • Multimodal models (varied types of input and outputs)
    • Multimodal models can do e.g. both text and image
• 💡 Smaller models: more cost-effective & more limited in outputs
• Amazon Titan
  • High-performing FM from AWS
  • Image, text, multimodal model choices via fully-managed APIs
  • Can be customized with your own data

Comparison table of important FMs

• Stable Diffusion used mainly for image generation
• Cheapness: Titan > Llama > Claude
• Input tokens: Claude > Titan > Llama
  • i.e. how big/long can the prompt be
• ‼️ Remember features and use cases!!
• 💡 The FMs are slowly converging to the same thing, but right now there are differences between them

DEMO

Ref: https://www.udemy.com/course/aws-ai-practitioner-certified/learn/lecture/44886347

• Can compare outputs of different FMs for same prompts
  • Screenshot
• Custom Models → Influence an FM with your own data:
  • Fine-tuning job: applied once
  • Continued Pre-training job: applied continuously
• Fine-tuning an FM
  • Uses S3 → Bedrock needs a service role with S3 permissions
    • Training data
    • Validation data (if existent)
    • Output data
  • Hyperparameters: present in ML training jobs
    • configurations on how the ML training algorithm should behave
    • very useful for data scientists
  • ❗ Must purchase provisioned throughput to run a fine-tuned model
    • Very expensive!!
    • 💡 You don't need to purchase Provisioned Throughput to actually DO the fine-tuning job (training), but you will have to purchase Provisioned Throughput to run the fine-tuned model (inference)
    • Screenshot