Amazon Bedrock - Overview

Introduction to Amazon Bedrock

Now that we've learned about Generative AI and foundation models, it's time to talk about Amazon Bedrock, the main service on AWS used to build generative AI applications.

Amazon Bedrock is a fully managed service, which means you don’t have to worry about managing the underlying infrastructure. It provides a simple way to access and interact with multiple foundation models through a unified interface.

Key Features of Amazon Bedrock

• Fully managed service:

  • No need to manage infrastructure
  • AWS handles everything behind the scenes

• Data privacy:

  • Your data stays within your AWS account
  • It is not used to retrain the underlying foundation models

• Pay-per-use pricing model:

  • You only pay for what you use
  • Pricing details will be discussed later

• Unified API:

  • One standardized method to interact with all foundation models
  • Simplifies application development

• Multiple foundation models available:

  • Easily choose and configure models from different providers

• Advanced features included:

  • RAG (Retrieval Augmented Generation)
  • LLM Agents
  • Security, privacy, governance, and responsible AI built-in in Amazon Bedrock

What type of Foundation Models are Available in Bedrock

Amazon Bedrock offers access to models from various top-tier AI providers:

• AI21 Labs
• Cohere
• Stability.ai
• Amazon
• Anthropic
• Meta
• Mistral AI

📌 More providers and models will continue to be added over time.

How Bedrock Handles Models

• When you use a foundation model:

  • Bedrock creates a copy of the model instance for your exclusive use
  • This ensures data isolation and privacy

• In some cases, you can fine-tune the model with your own data to better align it with your specific needs

• Again, none of your data is sent back to the original model providers

Bedrock Architecture Overview

Let’s visualize how Bedrock works, using a simplified diagram explained during the lecture:

Core Flow:

1. Users interact with an interactive playground

   • Users select the model to use

   • Input a question like:

     “What is the most popular dish in Italy?”

   • Model responds with an answer, for example:

     “Pizza and pasta”

2. We can have Knowledge Bases / RAG (Retrieval Augmented Generation)

   • This allows fetching external data to provide more accurate and relevant answers (will be covered in detail in later sections)

3. Model Fine-Tuning

   • You can upload and apply your own data to personalize the foundation model
   • All fine-tuning stays within your AWS account

4. Unified API Access

   • All apps communicate with Bedrock using a single API format
   • Bedrock manages model selection and orchestration behind the scenes

Summary

• Amazon Bedrock makes it easy to build, test, and deploy Gen AI applications using various foundation models.
• It gives you data privacy, scalability, fine-tuning, and a unified developer experience.
• In the next lecture, we’ll explore hands-on practice with Bedrock’s interactive playground.

Amazon Bedrock - Hands On