Cost-optimized and scalable enterprise workloads with Amazon Bedrock (AIM356)

Building Enterprise-Ready Generative AI Applications with Amazon Bedrock

Key Takeaways

  1. The industry has a significant gap in deploying generative AI in production, with only 24% of companies having workloads in production and 21% having a good governance and cost control framework.
  2. Amazon Bedrock simplifies the journey to building Enterprise-ready generative AI applications by addressing the key pillars of compliance and governance, cost and performance management, and operational excellence.
  3. The hybrid architecture approach, combining a centralized generative AI platform with decentralized workspaces, allows enterprises to balance governance and flexibility for their teams.
  4. Amazon Bedrock provides features to address each of these pillars, including model access and choice, security and compliance, cost tagging, cross-region routing, prompt engineering, and observability.

Compliance and Governance

  1. Model Access and Choice: Amazon Bedrock offers access to models from leading AI providers, with clear legal agreements (EOLAs) and compliance certifications.
  2. Security and Compliance: Bedrock provides data encryption, VPC connectivity, and customizable guardrails to filter harmful, sensitive, or irrelevant content.
  3. Governance and Control: Bedrock enables granular access control and cost management at the user, group, and project level.

Cost and Performance Management

  1. Cost Tagging: Bedrock allows tagging of resources, including on-demand invocations, to enable cost tracking and allocation across teams and projects.
  2. Cross-Region Routing: Bedrock's cross-region inference feature provides resilient and highly available application performance.
  3. Inference Modes: Bedrock offers batch, on-demand, and provisioned throughput inference modes to match the performance and cost requirements of different use cases.

Operational Excellence

  1. Prompt Engineering: Bedrock provides prompt templates, management, and optimization to help develop high-quality prompts efficiently.
  2. Bedrock Flows: Bedrock's low-code/no-code workflow builder allows orchestrating prompts, knowledge bases, agents, and guardrails into reusable application workflows.
  3. Observability: Bedrock integrates with Amazon CloudWatch for comprehensive metrics and logging, and also provides a custom observability solution for deeper insights into model invocations, latency, and more.

Generative AI Platform Architecture

  1. Centralized Generative AI Platform: This platform handles model onboarding, security, governance, cost management, and shared tooling.
  2. Decentralized Generative AI Workspaces: These provide development teams the flexibility to experiment, customize, and deploy applications using the centralized platform's capabilities.
  3. Integration between the Centralized Platform and Workspaces: The centralized platform exposes APIs and services that the decentralized workspaces can leverage, allowing a hybrid approach to balance governance and flexibility.

Additional Resources

  • Blog posts, articles, and a custom solution GitHub repository for implementing an access gateway with Amazon Bedrock.
  • Additional details on features and capabilities in the AWS Machine Learning blog.
  • Contact your AWS account team for further information and assistance.

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