What is Amazon Bedrock and How Do You Use It?

In this blog post you’ll learn:

• What is Amazon Bedrock?
• Amazon Bedrock's key features and capabilities
• Amazon Bedrock use cases and examples
• How to use Amazon Bedrock
• Amazon Bedrock security and responsible AI

Generative Artificial Intelligence (AI) is growing with almost unbelievable speed and almost every business is considering or even using generative AI. AWS (Amazon Web Services), as usual, isn't getting left behind, and AWS events always run great sessions about AI services.

Of the long list of AI services that you can use, we have selected Amazon Bedrock today because it’s one of the most anticipated.

In this blog post, we’ll take a look at what Amazon Bedrock is, its features, its use cases and some simple examples in real-world scenarios, and how to use it in the AWS console.

What is Amazon Bedrock?

Amazon Bedrock is one of the fully managed AWS services that offers a great set of features that leverage major FMs (Foundation Models) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon.

Currently Amazon Bedrock is only available in some regions, mainly US-EAST 1, EU-CENTRAL 1 and some other Asian, US and EU regions. You should first check the region that you need to use, because some regions don’t have access to all FMs.

Amazon Bedrock is currently the quickest way to build and scale generative AI applications in the AWS environment. Amazon Bedrock is serverless, so you can get started quickly without the need to manage any servers, as well as privately customize it with your data and easily integrate and deploy it into your applications. And because it’s a regional service, you can deploy it in the region closest to you or your users.

It also has some great advantages for those who already use AWS services, because you get a secure and simple connection between your current solutions to Bedrock.

Benefits of using Amazon Bedrock

• Ease of access: Bedrock simplifies access to powerful AI models, meaning businesses can quickly deploy sophisticated AI without needing deep expertise in its development.
• Scalability: By leveraging AWS infrastructure, AI applications built with Bedrock can scale as needed, handling large volumes of requests efficiently.
• Cost-effectiveness: Since Bedrock offers pre-trained models, companies save on the costs and resources associated with training large models from scratch.

Amazon Bedrock's key features and capabilities

The capabilities of Amazon Bedrock include the following:

• Foundation models that include a choice of base FMs and customized FMs
• Playgrounds for chat, text, and images with quick access to FMs for experimentation and use through the console
• Builder tools like knowledge bases, agents, prompt management and prompt flows
• Safeguards such as watermark detection and guardrails
• Model evaluation for assessment and deployment of the right FMs.

Let’s take a deeper look at the features that you will probably leverage when you want to start using Amazon Bedrock.

Foundation models

Pre-trained models: These models are already trained on vast amounts of data, making them powerful tools for a wide range of AI tasks without needing extensive additional training.

Customization and Fine-tuning: Users can fine-tune these models on their own data to better suit their specific needs, allowing them to create customized AI applications without starting from scratch.

Model evaluation

One of the major steps in selecting the correct AI model is its evaluation. This process can be difficult, but Amazon Bedrock already has a simple-to-use evaluation tool included in the AWS Management Console.

Playgrounds

This feature closely resembles classical generative AI models like ChatGPT or Gemini. It allows you to use chat, image, and text playgrounds to test FM models or use them as generative AI in the simplest way possible.

Chat playground

This helps you test the models for chat to assesst how they interact with your prompts or to just use them as normal generative AI models.

Text playground

If you need to generate text based on prompts, you can also test the specific models for this task.

Image playground

And yes you can also test out a couple of models for image generation or even use the images to your liking if you need to.

Builder tools

Knowledge bases

This is the quickest way to create a “database” for the FMs to retrieve information from. You can, for example, use a set of documents from the S3 buckets, which are accessed by the FMs when you prompt them. Essentially, knowledge bases make the FMs smarter by giving them access to detailed, business-specific information that you have already.

Agents

Amazon Bedrock agents can interact with the FMs, other AWS services, and external systems to perform tasks, make decisions, or handle requests.

Here is a simple example of how to use Amazon Bedrock agents:

Imagine you run an online store and want to automate customer support for order tracking.

1. Set up the agent: You create a Bedrock agent that can interact with an AI model trained to understand customer inquiries about orders.
2. Connect to data sources: The agent is connected to your order database and shipping service.
3. Process customer requests: When a customer asks, "Where’s my order?" the Bedrock agent uses the selected model to understand the request, and fetches and retrieves the latest tracking information from the shipping service.
4. Respond to the customer: The agent then generates and sends a response such as "Your order was shipped and is expected to arrive in two days."

Model Evaluation

With model evaluation on Amazon Bedrock, you can easily assess, compare, and choose the best foundational model for your needs.

Safeguards

Watermark Detection

Watermark Detection for Titan Image Generator identifies invisible watermarks embedded in every image the model creates. This watermark helps prevent the spread of disinformation, supports copyright protection, and tracks content usage.

Guardrails

Guardrails for Amazon Bedrock assesses user inputs and model responses according to specific use-case policies, adding an extra layer of protection regardless of the foundational model being used.

• You can, for example, detect and block harmful user inputs and model responses like hate and violence.
• Or you can create a specific topic that your FMs will not answer.
• Or you can add specific blocked words.

Amazon Bedrock use cases and examples

The best way to understand how Amazon Bedrock could be used in your business is by going through a set of use cases and examples from a real-world perspective.

Text generation: Craft original content like short stories, essays, social media posts, or website copy.
Real-world use case: A marketing team uses text generation to quickly create engaging social media content for product launches.

Virtual assistants: Develop assistants that can understand user requests, break down tasks, engage in conversations to gather information, and take actions to complete those tasks.
Real-world use case: A customer service chatbot is designed to handle inquiries, such as processing returns or tracking orders, reducing the need for human agents and improving response times.

Text and image search: Find and compile relevant information to answer questions and provide recommendations from extensive text and image data.
Real-world use case: A legal firm uses text and image search to quickly locate relevant case law and visual evidence from a vast database, helping lawyers prepare for court more efficiently.

Text summarization: Generate concise summaries of lengthy documents, such as articles, reports, research papers, technical manuals, and books, to quickly capture key information.
Real-world use case: A researcher uses text summarization to condense multiple lengthy research papers into brief overviews, allowing them to quickly review key findings without reading every detail.

Image generation: Create realistic, visually appealing images for ads, websites, presentations, and more in no time.
Real-world use case: A graphic designer generates high-quality images for an advertising campaign, speeding up the design process and enabling rapid iterations based on client feedback.

Guardrails: Set up safeguards tailored to your application's needs and AI policies for responsible usage.
Real-world use case: A healthcare application uses guardrails to ensure that generated responses to medical queries are accurate, safe, and comply with industry regulations, protecting both users and the organization.

How to use Amazon Bedrock?

Because of the variety of use cases, it’s hard to select the best way to start using Amazon Bedrock. Though it’s available through AWS Management Console and API, it's really easy to start in the Console. A probable first step for everybody is to enable the model access in the Bedrock Console, because by default you don’t have access to any FMs. It’s really simple: you just enable the models you need. There shouldn’t be any associated costs with doing so.

You can then access these models with all the features and functions previously described. I recommend first experimenting with the playgrounds to understand the models and how to interact with them. After that, you can follow some of the excellent workshops that AWS already offers for Amazon Bedrock.

If you want to play with Amazon AI, then also visit the AWS PartyRock page. It’s a really fun way to start learning!

Amazon Bedrock security and responsible AI

So let’s answer some common questions regarding the security of Amazon Bedrock, such as where is my data stored? Is my data stored and transferred securely and is it encrypted? Can I connect to Amazon Bedrock securely from an on-premises environment? Can I restrict access in the AWS environment to this service?

• Your provided data is not used to improve or enhance the base FMs. Your data is not shared with any model providers.
• As a regional service, Amazon Bedrock offers possibilities for storing data where you need it. The data that you store, including prompts, attachments that you use for prompts, AI responses and customized models remain in the selected region.
• All data is also encrypted in transit via TLS 1.2 and at rest with service-managed AWS Key Management Service (AWS KMS) keys.
• If you need to connect on-premises environments to Amazon Bedrock you simply leverage AWS PrivateLink.
• With AWS Identity and Access Management (IAM), you can manage who has access to your customized foundational models. You can decide which specific models and services are accessible and control who can log in to the Amazon Bedrock console.

Conclusion

So this was our first blogpost about Amazon Bedrock. We have described what it is, highlighted some of its features, explored simple use cases for multiple industries and explained how to start using it right now in the AWS Console.