Implementing ML-Driven Analyses with AWS Bedrock

Add Generative AI to your application with the help of AWS CDK and AWS SDK - Halia Haynes

In the rapidly evolving landscape of artificial intelligence, AWS Bedrock is a game-changer, making Generative AI accessible to both developers and businesses. This powerful platform provides the tools needed to build sophisticated generative AI applications with ease. One of the coolest things about AWS Bedrock is that it's totally stateless. That means that your data isn't stored anywhere on AWS's side. You don't have to worry about your sensitive information being collected or stolen by the model. You can rest easy knowing that your competitors can't prompt the model and accidentally stumble upon your internal data.

In this article, I will guide you through the process of implementing ML-driven analyses using AWS Bedrock, AWS CDK, and AWS SDK in TypeScript.

Let's Dive In

In this post, we'll cover:

  1. Considerations for model selection
  2. Setting up AWS Bedrock using AWS CDK
  3. Implementing Bedrock API calls using SDK
  4. Understanding key request parameters

AWS Bedrock

Which Model is Right for You?

Selecting the right model for your application is crucial. AWS Bedrock allows you to experiment with and evaluate different foundation models. Amazon Bedrock also offers users the flexibility to choose between model customization and pre-trained foundation models, catering to a wide range of use cases and requirements.

Here are some factors to consider when choosing a model:

  1. Task suitability: Different models excel at different tasks (e.g., general text generation, text summarization, question answering). Choose a model that aligns with your application's use case.
  2. Model size and performance: Larger models often provide better quality outputs but may be slower and more expensive. Consider the trade-offs between performance and cost.
  3. Pricing: Costs vary between models. Check out the AWS Bedrock pricing page for detailed information.

To get started with experimenting and evaluating different models, visit the Getting started with Amazon Bedrock guide.

Implementation Guide

Let's walk through the key steps to implement ML-driven analyses using AWS Bedrock:
NOTE: I will be using Anthropic's Claude 3 Haiku model throughout the code snippets.

1. Setting up AWS Bedrock in CDK v2
We'll use AWS CDK to define the Bedrock resource and related IAM permissions in our infrastructure-as-code setup:

const bedrockModel = bedrock.ProvisionedModel.fromProvisionedModelArn(
  this,
  "Model",
  "arn:aws:bedrock:{aws-region}::foundation-model/anthropic.claude-3-haiku-20240307-v1:0"
);
 
// Grant the appropriate permissions to interact with Bedrock
const bedrockPermissions = new iam.PolicyStatement({
  effect: iam.Effect.ALLOW,
  actions: ["bedrock:InvokeModel", "bedrock:ListFoundationModels"],
  resources: [
    "arn:aws:bedrock:*:*:foundation-model/anthropic.claude-3-haiku-20240307-v1*",
  ],
});
 
taskDefinition.addToTaskRolePolicy(bedrockPermissions);

NOTE: In this example, we're attaching the Bedrock permissions to an ECS Task Definition using taskDefinition.addToTaskRolePolicy(). However, you can grant these permissions to various AWS resources based on your application's architecture. For instance, you might attach them to a Lambda function, an EC2 instance, or any other compute option that interacts with AWS Bedrock.

NOTE: Even though AWS Bedrock is set up at the account level, access is controlled via IAM, so IAM permissions must be granted to the ECS task. Learn more about Identity and Access Management for Amazon Bedrock.

2. Prompting AWS Bedrock
Here's an example of how to invoke the Bedrock model using AWS SDK:

const command = new InvokeModelCommand({
  modelId: "anthropic.claude-3-haiku-20240307-v1:0",
  contentType: "application/json",
  accept: "application/json",
  body: JSON.stringify({
    anthropic_version: "bedrock-2023-05-31",
    messages: [
      {
        role: "user",
        content: prompt,
      },
    ],
    max_tokens: 500,
    temperature: 0.3,
    top_p: 0.8,
    top_k: 150,
  }),
});
const response = await this.client.send(command);

NOTE: Prompting in itself is a skill. Learn more to up your prompting game here.

Let's break down the key attributes that shape the model's output:

  • max_tokens: This is the length of the model's response. It sets the maximum number of tokens (words or word pieces) that the model will generate in its output. A lower max_tokens value (e.g., 50) will keep the model's responses brief and to the point, while a higher value (e.g., 500) allows the model to generate longer and more detailed responses.

  • temperature: This is the "randomness dial" of the model. It regulates how unpredictable the model's responses can be. A lower temperature (e.g., 0.2) makes the model more focused and likely to choose the most probable words, while a higher temperature (e.g., 0.8) encourages the model to take more risks and generate more surprising content.

  • top_p: This is the "confidence threshold" of the model. It sets the limit for the cumulative probability of the tokens the model considers. A smaller top_p value (e.g., 0.4) means the model will only look at the most confident choices, while a larger value (e.g., 0.95) allows the model to consider a broader range of options, even if they're less likely.

  • top_k: This is the "candidate pool size" of the model. It specifies the maximum number of highest probability tokens the model will evaluate at each step. A lower top_k value (e.g., 5) will make the model focus on the most likely candidates, while a higher value (e.g., 100) will let the model consider a more extensive set of possibilities, even if some are a bit unusual.

By tinkering with these settings, you can shape the model's output to match your application's needs. Looking for concise and focused responses? Set a lower max_tokens value, keep the temperature and top_p low, and top_k small. Want the model to generate more extensive and creative responses? Increase the max_tokens value, bump up the temperature and top_p, and choose a larger top_k.

In Summary

Implementing ML-driven analyses with AWS Bedrock offers a powerful way to integrate AI capabilities into your application. By leveraging AWS CDK for infrastructure setup and AWS SDK for model interaction, you can create sophisticated AI-powered features with ease.

Remember to consider model selection carefully, optimize your prompts, and be mindful of token usage and overall pricing. With these considerations in mind, you'll be well on your way to harnessing the power of generative AI in your projects!

  • Halia