Unlocking the Power of Generative AI with SageMaker Canvas
In the ever-evolving landscape of artificial intelligence (AI), enterprises are constantly seeking ways to leverage powerful foundation models (FMs) to drive innovation and productivity. With services like Amazon Bedrock and Amazon SageMaker JumpStart leading the way, the demand for granular control over model access has become paramount for compliance and governance.
Enter Amazon SageMaker Canvas, a groundbreaking visual point-and-click service introduced in 2021. This innovative tool empowers business analysts and citizen data scientists to harness the capabilities of both pre-built and custom machine learning (ML) models without the need for coding. By integrating a no-code interface, SageMaker Canvas revolutionizes the way generative AI models are accessed and deployed, accelerating productivity and democratizing AI knowledge.
In this blog post, we delve into the strategies for effectively governing access to Amazon Bedrock and SageMaker JumpStart models within the SageMaker Canvas environment using AWS Identity and Access Management (IAM) policies. By implementing tailored permissions, organizations can ensure secure and compliant usage of cutting-edge AI technologies, catering to a wide range of enterprise governance scenarios.
Empowering Control with IAM Policies
As the diagram illustrates, SageMaker Canvas provides a seamless interface for interacting with ML models, but it’s imperative to manage access effectively to uphold security standards. By customizing IAM policies attached to the SageMaker service role, organizations can regulate model invocation and endpoint provisioning within the Canvas environment.
Governing Amazon Bedrock Access
Amazon Bedrock models can be accessed through specific APIs like bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream. To restrict access, IAM policies can be crafted to deny these API calls, either for all Bedrock models or for specific ones based on model IDs.
Controlling SageMaker JumpStart Models
For SageMaker Canvas to handle LLMs from SageMaker JumpStart, meticulous permissions need to be configured for operations like model selection, deployment, and invocation. By leveraging IAM policies, organizations can restrict endpoint creation and deployment for all or specific JumpStart models, ensuring compliance with organizational guidelines.
Secure and Compliant AI Usage
By enforcing robust IAM policies tailored to your organizational needs, enterprises can navigate the complexities of generative AI access with confidence. This strategic approach not only safeguards sensitive data but also empowers users to leverage advanced AI capabilities within a controlled environment.
Conclusion
In conclusion, the integration of SageMaker Canvas with Amazon Bedrock and SageMaker JumpStart opens up a world of possibilities for enterprises looking to embrace generative AI. By implementing best practices in IAM policy management, organizations can ensure secure and compliant utilization of AI models, driving innovation and growth.
Authors
Davide Gallitelli – Senior Specialist Solutions Architect GenAI/ML
Lijan Kuniyil – Senior Technical Account Manager at AWS
Saptarshi Banerjee – Senior Partner Solutions Architect at AWS