Managing Foundation Model Lifecycle in Amazon Bedrock: Best Practices for Migration and Transition
Overview of Amazon Bedrock Model Lifecycle
Pricing Considerations During Extended Access
Communication Process for Model State Changes
Effective Migration Strategies and Best Practices
Planning Your Migration Timeline
Technical Migration Steps
Thorough Testing Strategies
Conclusion: Ensuring Continuity in AI Applications
About the Authors
Managing Foundations Models in Amazon Bedrock: A Comprehensive Guide
Amazon Bedrock is revolutionizing AI development with its foundation models (FMs). Regular updates enhance model capabilities, accuracy, and safety, which is great for businesses harnessing the power of AI. However, managing these evolving models requires a solid understanding of the model lifecycle to ensure seamless application performance.
In this post, we will walk you through how to effectively manage model transitions within Amazon Bedrock, ensuring your applications continue to thrive amidst regular updates. We’ll cover the model lifecycle states, the extended access feature for migrations, and practical strategies to facilitate transitions without disruptions.
Understanding the Amazon Bedrock Model Lifecycle
Models in Amazon Bedrock exist in three distinct states: Active, Legacy, and End-of-Life (EOL).
1. Active
Active models receive regular updates, maintenance, and bug fixes. They are fully operational for inference through APIs like InvokeModel or Converse. Customization is supported, and organizations can request quota increases via AWS Service Quotas.
2. Legacy
When a model transitions to Legacy, Amazon Bedrock provides at least 6 months’ notice before the EOL date, allowing ample time for migration plans. During the Legacy period, existing users can continue using the model, although it may become inaccessible for inactive accounts. Notably, new customers will not be able to access Legacy models, and certain customization capabilities might be restricted.
Public Extended Access Period: After remaining in Legacy for a minimum of 3 months, models enter an extended access phase. Here, existing users can continue access for another 3 months before EOL, though quota increase requests are generally not approved. Pricing alterations may also occur during this time, ensuring organizations plan ahead.
3. End-of-Life (EOL)
Once a model hits its EOL date, it becomes completely inaccessible across all AWS Regions, demanding immediate action from customers. Companies must proactively migrate applications to alternative models before EOL arrives, as the transition is not automatic.
It’s also essential to note that models remain available for at least 12 months post-launch and exist in the Legacy state for at least 6 months before EOL, providing a structured timeline for migration.
Pricing During Extended Access
During the extended access period, model providers may adjust pricing, with notifications relayed to customers. Those with private pricing agreements or provisioned throughput will retain their existing arrangements during this phase, reducing unexpected cost impacts.
Communication Process for Model State Changes
Customers will receive a notification 6 months prior to a model’s transition to Legacy state. This proactive communication approach ensures ample time for migration strategies. Notifications detail the following:
- Model deprecation specifics
- Key dates
- Extended access availability
- EOL timeline
Notifications are disseminated through various channels, including:
- AWS Health Dashboard
- Alerts in the Amazon Bedrock console
- API access
To ensure you receive these notifications, verify your account contact email addresses and consider setting up additional delivery channels for alerts.
Migration Strategies and Best Practices
When facing migrations, planning is paramount. Here are key strategies to ensure smooth transitions:
Planning Your Migration Timeline
Initiate planning as soon as a model enters the Legacy state:
- Assessment Phase: Examine current usage, identifying dependencies and issues with the legacy model.
- Research Phase: Investigate the recommended replacement model, noting differences and new capabilities.
- Testing Phase: Test the new model thoroughly to gauge performance compared to the previous version.
- Migration Phase: Implement a phased deployment approach, monitoring system performance throughout.
- Operational Phase: Post-migration, continue to monitor the application for expected performance.
Technical Migration Steps
Thoroughly test and implement the following steps:
- Update API References: Change application code to reference the new model.
- Request Quota Increases: Ensure sufficient quotas for the new model.
- Adjust Prompts: Review and refine prompts to optimize model performance.
- Update Response Handling: Modify parsing logic based on new response formats.
- Optimize Token Usage: Take advantage of newer models’ efficiencies.
Testing Strategies
Conduct thorough testing to validate functionality:
- Side-by-Side Comparisons: Run requests against both old and new models to identify discrepancies.
- Performance Testing: Measure response times and other vital performance metrics.
- Regression and Edge Case Testing: Ensure existing functionalities work correctly in all scenarios.
Conclusion
Amazon Bedrock’s model lifecycle provides clear stages for managing FM evolution. By leveraging the extended access options available, organizations can balance innovation with continuous service stability.
By staying informed, planning migrations proactively, and testing thoroughly, businesses can maintain operational continuity while exploiting the latest advancements in foundation models.
If you have questions or need assistance, don’t hesitate to reach out to your AWS team. For additional learning and implementation resources, explore the official AWS Bedrock documentation, and stay updated through the AWS Machine Learning Blog and AWS Architecture Center.
About the Authors
Saurabh Trikande is a Senior Product Manager focused on democratizing AI through Amazon Bedrock and SageMaker. He enjoys hiking and exploring innovative technologies.
Melanie Li, PhD, is a Senior Generative AI Specialist Solutions Architect passionate about working with customers on cutting-edge AI/ML solutions.
Derrick Choo accelerates digital transformation through AI/ML solutions and has a strong focus on end-to-end systems.
Jared Dean collaborates with industries to enhance efficiency through machine learning applications, driven by a passion for technology and barbecue.
Julia Bodia serves as Principal Product Manager for Amazon Bedrock, focusing on product development strategies.
Pooja Rao is a Senior Program Manager leading quota and capacity initiatives for the Bedrock Go-To-Market team, finding joy in reading and travel.
Feel free to adapt this blog post for your needs, adding personal insights or specific use cases that resonate with your audience!