Streamlining AI Development with Amazon Bedrock: A Deep Dive into Model Share and Model Copy Features

Introduction to Generative AI in Organizations

Prerequisites for Model Copy and Model Share

Model Share: Streamlining Development-to-Production Workflows

Model Copy: Optimizing Model Deployment Across Regions

Aligning Model Share and Model Copy with AWS Best Practices

From Development to Production: A Practical Use Case

Conclusion: Enhancing AI Lifecycle Management

About the Authors

Streamlining AI Development with Amazon Bedrock: An In-Depth Look at Model Share and Model Copy

In the rapidly evolving landscape of generative AI, organizations are increasingly adopting structured approaches to deploy their AI applications, mirroring traditional software development practices. This systematic methodology typically involves distinct development and production environments, often utilizing separate AWS accounts for each. This separation not only enhances security but also streamlines workflows, creating a logical and efficient deployment process.

Enter Amazon Bedrock

Amazon Bedrock stands as a fully managed service that provides access to high-performing foundation models (FMs) from industry leaders such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. With a single API, organizations can harness a range of capabilities to build generative AI applications, keeping security, privacy, and responsible AI at the forefront.

However, as organizations scale their AI initiatives, they encounter challenges in managing and deploying custom models across different development stages and geographical regions. To address these challenges, Amazon Bedrock has introduced two crucial features: Model Share and Model Copy.

Understanding Model Share and Model Copy

These features streamline the AI development lifecycle, facilitating smooth collaboration between development and production teams while optimizing resource utilization. Let’s dive into each feature in detail.

Prerequisites for Model Copy and Model Share

Before you can utilize Model Copy and Model Share, you must meet several prerequisites:

• AWS Organizations Setup: The source and target accounts need to be part of the same AWS Organization.
• IAM Permissions: Appropriate permissions must be granted.
• KMS Key Policies (Optional): If models are encrypted with a customer-managed Key Management Service (KMS) key, policies must be set to allow target accounts to decrypt or encrypt the models.
• Network Configuration: Ensure the right configurations, especially if you’re using VPC endpoints.
• Service Quotas: Check if quotas for custom models per account are met and request increases if necessary.
• Provisioned Throughput Support: Ensure that the target Region supports the model’s provisioned throughput.

Model Share: Streamlining Development-to-Production Workflows

Model Share allows seamless sharing of custom models fine-tuned on Amazon Bedrock between different AWS accounts within the same Region and organization. This feature is invaluable for organizations that separate development and production environments.

Key Benefits:

• Simplified Transitions: Quickly move fine-tuned models to production environments.
• Enhanced Collaboration: Collaborate across various departments or project teams.
• Resource Optimization: Reduce duplicated customization efforts organization-wide.

How It Works:

After fine-tuning a model in the source AWS account, the model can be shared with the target AWS account via the AWS Management Console. Once accepted, the model can be copied to necessary Regions for deployment.

Important Considerations:

• Both accounts must be in the same organization.
• Only models fine-tuned within Amazon Bedrock can be shared.
• Implement proper KMS key policies if sharing encrypted models.

Model Copy: Optimizing Model Deployment Across Regions

Model Copy enables organizations to replicate custom models across different Regions within a single account. This feature can be used for single-account deployments or complement Model Share scenarios, particularly for global model distribution and robust disaster recovery solutions.

Key Benefits:

• Reduced Latency: Deploy models closer to end-users to minimize response times.
• Increased Availability: Enhance the reliability of AI applications through model accessibility in various Regions.
• Improved Disaster Recovery: Maintain model replicas, facilitating easier disaster recovery strategies.

How It Works:

Identify the target Region for your model, initiate the Model Copy process from the source Region using the Amazon Bedrock console, and purchase provisioned throughput after copying the model.

Aligning with AWS Best Practices

When using Model Share and Model Copy, it’s crucial to align these features with AWS best practices for multi-account environments. Some key considerations include:

• Maintaining compliance with any policies set at the organizational unit (OU) level.
• Effectively integrating these features into your CI/CD pipeline.
• Managing costs across accounts with AWS billing features.

A Practical Use Case

To illustrate the practical application of Model Share and Model Copy, let’s walk through a typical scenario:

Step 1: Model Development (Development Account)
Data scientists fine-tune a model using varied foundation models, followed by prompt engineering, performance evaluation, and ensuring ethical compliance via Amazon Bedrock Guardrails.

Step 2: Model Evaluation and Selection
The team evaluates the fine-tuned model’s performance to confirm it’s production-ready.

Step 3: Model Sharing (Development to Production Account)
Upon approval, the development team shares the model with the production account using Model Share.

Step 4: Model Copy (Production Account)
After receiving the shared model, the production team must copy it to their desired Region for usage.

Step 5: Production Deployment
Finally, the production team can purchase provisioned throughput and set up the necessary infrastructure for inference.

Conclusion

Amazon Bedrock’s Model Copy and Model Share features provide a powerful arsenal for managing an AI application’s lifecycle from development to production. These tools enable organizations to streamline transitions, enhance collaboration, optimize global model performance, and maintain compliance throughout the model lifecycle.

As AI technology continues to advance, adopting these best practices will ensure organizations remain agile, efficient, and competitive. A successful journey from development to production requires continuous evaluation, monitoring, and refinement of models to align with business needs. With Amazon Bedrock, you’re well-equipped to navigate these challenges effectively.

About the Authors

Ishan Singh is a Generative AI Data Scientist at AWS, specializing in building innovative generative AI solutions.

Neeraj Lamba is a Cloud Infrastructure Architect at AWS, helping customers transform their businesses through cloud solutions.

By integrating Model Share and Model Copy into your development process, you can harness the full potential of AI in a structured and secure manner.