Generative AI has become the disruptor in all sectors, driving progress in natural language processing, computer vision, and many other areas. However, using its potential has often been accompanied by challenges of costs, complex infrastructure, and steep learning curves. That’s where AWS Bedrock comes in – this solution helps unblock by allowing you to use foundation models without the need to manage infrastructure.

This tutorial aims to be your complete guide to Amazon Bedrock, describing what it is, how it works, and how you can use it. By the end of this guide, you’ll have the information and skills you need to develop your own generative AI applications—scalable, flexible, and aligned to your goals.

What is Amazon Bedrock?

For instance, imagine you are developing a customer support chatbot. With AWS Bedrock, you can select a sophisticated language model, tune it for your application’s needs, and embed it in your application without ever having to write server configuration in code.

Features of AWS Bedrock

AWS Bedrock’s features are designed to simplify and accelerate the journey from AI concept to production. Let’s break them down in detail.

Access to multiple foundation models

One of the most significant benefits of AWS Bedrock is the variety of foundation models available. Whether you are working on text applications, visual content, or safe and interpretable AI, Bedrock has you covered. Here are some of the models available:

• Amazon models: These proprietary models can be used for tasks like creating human-like text for chatbots or content creation applications, reducing the length of documents to their essence, or interpreting the sentiment of customer feedback.

• Anthropic models: These models focus on developing good, safe artificial intelligence and are most suitable for industries that require compliance and trust, such as banking or healthcare.

• Stability AI: Stability AI models are well known for their image generation. They help transform ideas into visuals for marketing, artwork, or product development.

Diagram of Amazon Bedrock, connecting to Amazon Models, Anthropic, Stability AI, and other providers, illustrating its role as a hub for generative AI services.

Overview of Amazon Bedrock, highlighting its integration with models.

No infrastructure management

AWS Bedrock abstracts infrastructure management, which means:

• No need to provision GPU instances.

• You can focus purely on building applications with a serverless architecture.

• The ease of use reduces setup times significantly, often from weeks to hours.

Scalability and flexibility

AI generative applications typically have an unpredictable demand. A chatbot may respond to hundreds of users during peak hours and only a few at night. But AWS Bedrock solves this with its built-in scalability:

• Automatic scaling: This means models can automatically adjust to handle workload spikes without you having to intervene manually.

• Parallel workloads: You can run multiple models at once for different use cases within a single application.

• Global availability: With AWS’s global network, you can deploy your applications closer to your users, cutting latency and enhancing user experience.

Integration with the AWS ecosystem

AWS Bedrock goes beyond providing powerful models—it integrates with other AWS services to support end-to-end AI workflows. Some integrations include:

• Amazon SageMaker: Enables fine-tuning of foundation models to meet specific requirements.

• AWS Lambda: Facilitates event-driven AI applications, such as triggering a model to fine-tune new data or review inference results.

• Amazon CloudWatch: Provides monitoring and logging capabilities for analyzing model performance and gathering user feedback.

• Amazon S3: Serves as a storage solution for datasets, enabling performance tracking and cost analysis.

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Setting Up AWS Bedrock

This section will guide you through setting up the necessary permissions, creating an AWS account, and getting started with AWS Bedrock.

Step 1: Creating an AWS account (if not already done)

AWS sign-up page with a form to input a root user email address and AWS account name.

The AWS sign-up page, showcasing Free Tier product exploration for new accounts.

Step 2: Navigating to AWS Bedrock

Amazon Bedrock is accessible through the AWS Management Console. Follow these steps to locate and start using it:

• Log in to the AWS Management Console: Navigate to AWS Console and enter your login credentials.

• Search for Bedrock: Use the search bar at the top of the console. Type “Bedrock” and select it from the dropdown.

Screenshot of AWS Management Console search results for "Bedrock," displaying Amazon Bedrock as the service for building generative AI applications, Amazon SageMaker for data and AI analytics, and a highlighted introduction to resource search with cross-region capabilities

AWS Management Console search results for Bedrock.

• Explore the Bedrock dashboard: Once on the Bedrock dashboard, you’ll see options for choosing model providers and foundation models.

Screenshot of the Amazon Bedrock Providers page featuring three serverless model providers: Amazon, Anthropic, and Meta

Amazon Bedrock Providers page showcasing serverless model options from Amazon.

• Select a model provider and a foundation model:

  • Choose a provider based on your use case (e.g., Amazon Titan for text generation, Stability AI for images, etc.).

  • Explore model-specific options like input types, supported features, and output parameters.

• Run a test inference: AWS Bedrock allows you to run sample inferences directly from the console. This is a great way to get a feel for how each model works before integrating it into your application.

Screenshot of the Amazon Bedrock Chat/Text Playground, featuring the Claude 3 Opus model. The interface includes options to input a prompt, run responses, and compare different models. The left panel lists options like "Getting started," "Foundation models," "Playgrounds," "Builder tools," and "Safeguards."

Amazon Bedrock Chat/Text Playground interface.

Step 3: Setting up IAM permissions

AWS Identity and Access Management (IAM) is critical for securely accessing AWS Bedrock. Follow these steps to configure permissions:

• In the AWS Management Console, navigate to the IAM service.

• Click Roles in the sidebar and select Create policy.

• In Specify permissions, choose “JSON”.

Screenshot of the AWS IAM Policy Editor in JSON mode, showing a partially configured policy with an empty "Action" and "Resource" list. The left sidebar indicates a two-step process: "Specify permissions" and "Review and create." The right panel provides options to add actions, filter services, and remove statements.

AWS IAM Policy Editor in JSON mode

• Paste the below policy in the text box:

{
    "Version": "2012-10-17",
    "Statement": [        {\
            "Sid": "BedrockFullAccess",\
            "Effect": "Allow",\
            "Action": ["bedrock:*"],\
            "Resource": "*"\
        }\
    ]
}

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Note: The policy above can be attached to any role that needs to access the Amazon Bedrock service. It can be SageMaker or a user. When using Amazon SageMaker, the execution role for your notebook is typically a different user or role than the one you use to log in to the AWS Management Console. To find out how to explore  Amazon Bedrock service using the AWS Console, ensure you authorize your Console user or role. You can run the notebooks from any environment with access to the AWS Bedrock service and valid credentials.

Using Amazon Bedrock for Generative AI Applications

Generative AI applications are built upon foundation models that are fine-tuned for a particular task, such as text generation, image creation, or data transformation. Following is a step-by-step guide on choosing a foundation model, using basic inference jobs, and modifying model responses to suit your needs.

Selecting a foundation model

Picking the right foundation model is important because it depends on what your project needs. Here’s how to make a selection:

1. Identify your use case:

• Text generation: For tasks like  summarization, content creation, or chatbot development, consider models like:

  • Amazon Titan Text G1: This model generates high-quality text with a good understanding of the context.

  • Anthropic Claude 3: This model is very good at producing text that is coherent and relevant to the context and is thus suitable for conversational AI applications.

• Image generation: If your project is about creating images or any  form of visual content, then Stability AI models are the way to go:

  • Stable Diffusion 3.5 Large: This model is very good at generating pictures from text descriptions, whether photorealistic or artistic.

• Multimodal tasks: For applications that need both text and image processing, Amazon Nova models are recommended:

  • Nova Lite: A cost-sensitive multimodal model that can take text, image, and video input and output text.

  • Nova Pro: A competent multimodal model for more complex tasks.

1. Evaluate model capabilities:

• Review the specific strengths of each model to ensure alignment with your project's needs. Detailed information about supported foundation models, including their capabilities and supported regions, is available in the AWS documentation.

Enabling model access

Before using these models, you need to enable model access within your AWS account. Here are the steps to set it up:

• In the Bedrock Console, navigate to Models access.

• In the Console, click on Modify model access.

• Browse the available models from providers and select the one you like. In this case, I selected Titan Text G1—Express.

Screenshot of the Amazon Bedrock "Edit model access" page, showing a list of Amazon Titan models with their access statuses. Some models have "Access granted," while others are "Available to request." The interface includes options to filter models, expand or collapse sections, and group models by provider. The left panel shows a two-step process: "Edit model access" and "Review and submit."

Amazon Bedrock model access management page.

• Click Next, and then click on Review and submit.

Running basic inference

To perform inference using a selected foundation model in AWS Bedrock, follow these steps:

• Set up the AWS SDK for Python (boto3):

pip install boto3

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• Initialize the Bedrock client: Create a Bedrock runtime client in the AWS region of your choice:

python

import boto3
import json
from botocore.exceptions import ClientError

# Set the AWS Region
region = "us-east-1"

# Initialize the Bedrock Runtime client
client = boto3.client("bedrock-runtime", region_name=region)

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• Invoke the model:Define the model ID and the input prompt:

python

# Define the model ID for Amazon Titan Express v1
model_id = "amazon.titan-text-express-v1"

# Define the input prompt
prompt = """
Command: Compose an email from Tom, Customer Service Manager, to the customer "Nancy"
who provided negative feedback on the service provided by our customer support
Engineer"""

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• Format the request payload:

python

# Configure inference parameters
inference_parameters = {
   "inputText": prompt,
   "textGenerationConfig": {
       "maxTokenCount": 512,  # Limit the response length
       "temperature": 0.5,    # Control the randomness of the output
   },
}

# Convert the request payload to JSON
request_payload = json.dumps(inference_parameters)

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• Process the response:

python

try:
   # Invoke the model
   response = client.invoke_model(
       modelId=model_id,
       body=request_payload,
       contentType="application/json",
       accept="application/json"
   )

   # Decode the response body
   response_body = json.loads(response["body"].read())

   # Extract and print the generated text
   generated_text = response_body["results"][0]["outputText"]
   print("Generated Text:
", generated_text)

except ClientError as e:
   print(f"ClientError: {e.response['Error']['Message']}")
except Exception as e:
   print(f"An error occurred: {e}")

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You can expect the below output:

bash

% python3 main.py

Generated Text:

Tom:
Nancy,

I am writing to express my sincere apologies for the negative experience you had with our customer support engineer. It is unacceptable that we did not meet your expectations, and I want to assure you that we are taking steps to prevent this from happening in the future.

Sincerely,
Tom
Customer Service Manager

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Customizing the model outputs

• temperature: This parameter controls the randomness of the output. Lower values increase the determination of the output, and higher values increase the variability.
• MaxTokenCount: Sets the maximum length of the generated output.

For example:

python

inference_parameters = {
   "inputText": prompt,
   "textGenerationConfig": {
       "maxTokenCount": 256,  # Limit the response length
       "temperature": 0.7,    # Control the randomness of the output
   },
}

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By adjusting these parameters, you can better tailor the creativity and length of the generated content to your application's needs.

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Advanced Amazon Bedrock Features

Let’s switch gears and focus on two advanced approaches: enhancing AI using Retrieval-Augmented Generation (RAG) and managing and deploying models at scale.

Customizing AI outputs with retrieval-augmented generation (RAG)

RAG requires us to have a knowledge base. Before setting up the knowledge base in Amazon Bedrock, you need to create an S3 bucket and upload the required files. Follow these steps:

Step 1: Create an S3 bucket

• Log in to the AWS Management Console:

  • Go to the S3 Service in the AWS Management Console.

• Create a new bucket:

  • Click Create bucket.

  • Enter a unique bucket name.

  • Leave default settings unless you have specific security or encryption requirements.

• Configure the bucket settings:

  • Ensure that block public access is enabled unless you require it.

  • (Optional) Enable versioning if you want to maintain versions of your files.

• Create the bucket:

  • Click Create bucket to finalize the setup.

Step 2: Upload files to the S3 bucket

• Download the file:

  • The provided GitHub link provides a sample PDF file with mock data: octank_financial_10K.pdf. You can use a different file if you’d like.

  • Save it locally on your computer.

• Upload the file to S3:

  • Open the newly created S3 bucket in the AWS Console.

  • Click Upload and select the file octank_financial_10K.pdf.

  • Review and set the permissions (default: private).

  • Click Upload to add the file to the bucket.

Screenshot of an Amazon S3 bucket named "amazon-bedrock-099," displaying the "Objects" tab with a single PDF file named "octank_financial_10K.pdf."

Amazon S3 bucket view for "amazon-bedrock-099".

Step 1: Provide the knowledge base details

• Navigate to the Amazon Bedrock Console and select Knowledge Bases under the Builder tools section.

• Click Create Knowledge Base and fill out the following details:

  • Knowledge Base name: Enter a unique name (e.g., knowledge-base-quick-start).

  • Knowledge Base description: (Optional) Briefly describe the knowledge base.

• In the IAM permissions section:

  • Choose “Create and use a new service role” or “Use an existing service role”.

  • Ensure the service role has the necessary permissions for Bedrock access.

  • Example Service Role: AmazonBedrockExecutionRoleForKnowledgeBase.

• Click Next to proceed with configuring the data source.

Screenshot of the Amazon Bedrock Knowledge Base creation interface, showing steps to configure a knowledge base.

Amazon Bedrock Knowledge Base creation wizard.

Step 2: Configure the data source

• Under the Data source details section, choose your data source:

  • Amazon S3: Select this option as your data is stored in S

• Specify the following:

  • Data source location: Choose between "This AWS Account" or "Other AWS Account."

  • S3 URI: Provide the S3 bucket URI containing the data. Choose the S3 you created in the previous step and the file.

  • Encryption key: (Optional) Add a customer-managed KMS key if your data is encrypted.

• Configure the parsing strategy:

  • Default parser: Processes plain text data. You can leave it as is.

  • Foundation models as parser: Processes complex documents or images. You can leave it as is.

• Chunking strategy:

  • The default setting splits text into chunks of approximately 300 tokens for embeddings. You can leave it as is.

• (Optional) Add Transformation functions or advanced settings to preprocess your data. You can leave it as is.

• Click Next to proceed.

Screenshot of the Amazon Bedrock data source configuration interface, showing settings for connecting an Amazon S3 data source

Amazon Bedrock data source configuration page for integrating an S3-based knowledge base.

Step 3: Select the embeddings model and configure the vector store

• Choose an Embeddings model to convert your data into vector representations:

  • Titan Embeddings G1

• Text v1.2  (You can choose this for this tutorial.)

• Embed English v3

• Embed Multilingual v3

• Under the Vector database section:

  • Choose “Quick create a new vector store” (recommended) or select an existing store.

  • Options include:

    • Amazon OpenSearch Serverless: This is ideal for contextually relevant responses. You can choose it for the purpose of this tutorial.

    • Amazon Aurora PostgreSQL Serverless: Optimized for fast similarity searches.

    • Amazon Neptune Analytics: Suitable for graph-based analytics and Retrieval-Augmented Generation (RAG).

• Click Next to review and finalize your configuration.

Screenshot of the Amazon Bedrock interface for selecting an embeddings model and configuring a vector database.

Amazon Bedrock configuration page for selecting an embedding model and vector store.

Step 4: Review and create

• Review all your configurations.

• Confirm and click Create to finalize the knowledge base.

• Once created, your knowledge base will appear under the Knowledge Bases section in the console.

Step 5: Test the knowledge base

• Access the knowledge base overview:

  • Go to the Knowledge Bases section in the Amazon Bedrock Console.

  • Select the knowledge base you created (e.g., knowledge-base-quick-start).

  • Review the overview details:

    • Knowledge Base name and ID

    • Ensure it is set to “Available”.

    • Verify the correct service role is in use.

• Sync and add data sources:

  • If the data source hasn’t been synced, click Sync to initiate the process.

  • You can also add more documents or data sources via Add documents from S

Screenshot of the Amazon Bedrock Knowledge Base overview page, displaying key details such as the knowledge base name, ID, service role, status, and retrieval-augmented generation (RAG) type.

Amazon Bedrock Knowledge Base overview shows configuration details and data source status.

Step 6: Select a model for testing

• Choose a foundation model:

  • Click Select model in the Test Knowledge Base panel.

  • Browse the available models from providers like Amazon, Anthropic, and Cohere.

  • Example: Select Claude Instant (v1.2) for text-based queries.

• Inference type:

  • Choose On-demand inference if you don’t have provisioned throughput set up.

• Apply the model:

  • Click Apply to finalize your model selection.

Screenshot of the Amazon Bedrock "Select model" interface, displaying model providers including Amazon, Anthropic, Cohere, Meta, and Mistral AI.

Amazon Bedrock model selection interface features various Claude models from Anthropic.

Step 7: Run queries and generate responses

• Generate a response:

  • Enter your query in the input box (e.g., “Help me with Financial Statement Schedules”).

  • Click Run to test the knowledge base.

• View source details:

  • The response will include chunks of data relevant to your query.

  • Expand Source Details to view metadata and the source of the retrieved information.

• Review the output:

  • Validate the response to ensure it aligns with the knowledge base content.

  • Fine-tune retrieval configurations if necessary.

Screenshot of the Amazon Bedrock Knowledge Base testing page. The selected model is "Claude Instant v1.2" set to on-demand inference.

Amazon Bedrock Knowledge Base testing interface displaying a response for a query.

Example query output:

• Query: “Help me with Financial Statement Schedules.”
• Response: The knowledge base will retrieve data detailing specific line items in financial statements, such as schedules of assets, liabilities, and related metadata.

Managing and deploying AI models at scale

Using AWS services like Lambda, Amazon Bedrock can manage and deploy AI models at scale. This cost-efficient approach ensures high availability and automatically scales for AI-powered applications.

In this section, we will use AWS Lambda to invoke a Bedrock model dynamically so you can process prompts on demand.

Step 1: Create an AWS Lambda function

• Log in to the AWS Management Console.

• Navigate to the Lambda service and click Create function.

• Choose the function configuration:

  • Select “Author from Scratch”.

  • Function name: bedrock.

  • Runtime: Python 3.x.

  • Permissions: Select an IAM Role that has been created in the initial section of this tutorial.

• Click Create function to initialize the setup.

Screenshot of the AWS Lambda function creation interface. The selected option is "Author from scratch," and the function name is set to "bedrock."

AWS Lambda function creation page.

Step 2: Add the Lambda function code

• Open the function editor in the Code section of the Lambda console.

• Replace the default code with the code provided in the Github Gist.

• Click Deploy to save the changes.

Step 3: Test the Lambda function

• Go to the Test tab in the Lambda console and create a test event with the following JSON:

none

{
    "prompt": "Write a formal apology letter for a late delivery."
}

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• Save the test event.

• Click Test to invoke the Lambda function.

• Check the output in the Execution results section. The generated text should look like this:

Screenshot of AWS Lambda execution results indicating a successful function execution

AWS Lambda execution result showing a successful function run.

Best practices for AI model deployment

Optimize cost:

• Use on-demand inference for occasional use cases.

• Consider provisioned throughput if high-frequency requests are expected.

Secure the Lambda function:

• Restrict the IAM role to only the required permissions.

• Use environment variables to avoid exposing sensitive data in code.

Monitor and log:

• Use CloudWatch logs to track invocation metrics and debug issues.

• Set up alarms for errors or high latency.

Best Practices for Using AWS Bedrock

In this section, I share some best practices when working with Amazon Bedrock, from optimizing costs to keeping things secure and accurate.

Cost optimization

Managing costs in AWS Bedrock may involve leveraging Amazon SageMaker to deploy models and utilize Spot instances to save up to 90% on expenses. Here are some of my best practices:

• Choose the right foundation model: Select models that align with your needs. Choose smaller, less resource-intensive models to reduce compute costs for less complex tasks. For example, Amazon Titan Models can be used for text-based tasks like summarization or Stability AI for simple image generation.

• Batch inference: Instead of processing requests individually, group multiple inference requests into batches. This reduces the frequency of model invocations, leading to lower costs.

• Leverage S3 lifecycle policies: Store only the data you need for inference or fine-tuning in Amazon S

1. Use lifecycle policies to automatically move data to cheaper storage tiers (e.g., S3 Glacier) or delete unused data after a set time.

• Monitor and control spending: Use AWS Cost Explorer to analyze your spending patterns and identify areas for optimization. Set AWS Budgets to receive alerts when you approach predefined cost thresholds.

Graph depicting the cost and usage of Amazon Bedrock services

Cost and usage graph for Amazon Bedrock services.

• Use Bedrock with SageMaker for cost efficiency: Use Bedrock foundation models in SageMaker to take advantage of managed Spot instances for inference and training jobs.

• Use Spot instances in SageMaker: Spot instances can reduce costs by up to 90% compared to On-Demand instances. SageMaker handles Spot interruptions on your behalf, automatically retrying training jobs if Spot capacity becomes unavailable. Example:

python

from sagemaker.mxnet import MXNet

# Use spot instances for cost efficiency
use_spot_instances = True

# Maximum runtime for the training job (in seconds)
max_run = 600

# Maximum wait time for Spot instance availability (in seconds); set only if using Spot instances
max_wait = 1200 if use_spot_instances else None

# Define the MXNet estimator for the training job
mnist_estimator = MXNet(
    entry_point='source_dir/mnist.py',  # Path to the script that contains the model training code
    role=role,  # IAM role used for accessing AWS resources (e.g., S3, SageMaker)
    output_path=model_artifacts_location,  # S3 location to save the trained model artifacts
    code_location=custom_code_upload_location,  # S3 location to upload the training script
    instance_count=1,  # Number of instances to use for training
    instance_type='ml.m4.xlarge',  # Instance type for the training job
    framework_version='1.6.0',  # Version of the MXNet framework to use
    py_version='py3',  # Python version for the environment
    distribution={'parameter_server': {'enabled': True}},  # Enable distributed training with a parameter server
    hyperparameters={  # Training hyperparameters
        'learning-rate': 0.1,  # Learning rate for the training job
        'epochs': 5  # Number of training epochs
    },
    use_spot_instances=use_spot_instances,  # Enable Spot instances for cost savings
    max_run=max_run,  # Maximum runtime for the training job
    max_wait=max_wait,  # Maximum wait time for Spot instance availability
    checkpoint_s3_uri=checkpoint_s3_uri  # S3 URI for saving intermediate checkpoints
)

# Start the training job and specify the locations of training and testing datasets
mnist_estimator.fit({
    'train': train_data_location,  # S3 location of the training dataset
    'test': test_data_location  # S3 location of the testing/validation dataset
})

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• Use CloudWatch metrics and logs: Training metrics and logs are available in Amazon CloudWatch, providing insights into performance and resource usage.

Screenshot of AWS CloudWatch log events, displaying entries for a machine learning model under the path /opt/ml/model. Each log includes a timestamp, source IP, and HTTP GET request to /ping with a 200 status code.

AWS CloudWatch logs

Security

To ensure the security, compliance, and performance of Amazon Bedrock, it is important to follow the best practices. Here are my recommendations:

• Apply least privilege access: Restrict IAM permissions and service roles to only the necessary resources to prevent unauthorized access.

• Validate service roles and security groups: Ensure Bedrock agents and model customization jobs are correctly configured with active service roles and security settings.

• Run workloads in a VPC: Increase security by running customization jobs and data processing within a Virtual Private Cloud (VPC).
• Encrypt critical data: Use Amazon KMS Customer-Managed Keys (CMKs) to encrypt agent sessions, Bedrock Studio workspaces, custom models, and transient knowledge base data.

• Implement data deletion policies: Retain vector-stored data only when knowledge-based sources are active; set up automatic deletion when no longer needed.

• Strengthen Bedrock guardrails: Set Prompt Attack Strength to HIGH and enable sensitive information filters to protect against adversarial attacks and data leaks.

• Secure agent sessions: Use guardrails to prevent unauthorized access and protect sensitive interactions.

• Enable logging for visibility: Turn on model invocation logging at the account level to track activity and detect anomalies.

• Defend against cross-service attacks: Use service role policies to prevent Cross-Service Confused Deputy Attacks.

Model monitoring and evaluation

Ensuring that models are accurate, reliable, and aligned with business objectives is achieved by monitoring and evaluating models. Here are some best practices:

• Track performance metrics: Use CloudWatch to monitor latency, errors, and invocation trends.

• Log inputs and outputs: Enable detailed logging for debugging and trend analysis.

• Incorporate feedback loops: Use tools like Amazon A2I to include human reviews for critical outputs.

• Evaluate outputs regularly: Compare outputs with validation datasets or ground truth.

• Continuously improve models: Fine-tune models with updated datasets or specific domain data as needed.

Dashboard with four graphs tracking metrics for Amazon Bedrock models.

The metrics dashboard displays the invocation count, latency, and token counts for various models in Amazon Bedrock.

Conclusion

AWS Bedrock is a game-changer in how generative AI applications are developed and a centralized platform for using foundation models without worrying about the infrastructure.

From this point forward, this step-by-step tutorial should help you identify the right models, create secure and scalable workflows, and integrate features like retrieval-augmented generation (RAG) for greater customization. With the best practices for cost, security, and monitoring in place, you are ready to develop and manage your AI solutions to meet your objectives.

To further deepen your AWS expertise, explore these courses:

• AWS Cloud Technology and Services – Learn about AWS's core services and how to build cloud-native solutions.
• AWS Security and Cost Management – Master strategies for securing your AWS environment and optimizing costs effectively.
• Developing AI Applications – Dive into AI development and learn how to build, deploy, and scale AI-powered solutions.

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