Cloud AI for Innovative Applications and Uses
Introduction
Cloud AI is transforming enterprises by enabling the development of innovative applications that enhance customer and employee experiences. Organizations are leveraging cloud AI for intelligent document processing, real-time translation, automated summarization, personalized marketing, image and code generation, and customer support automation. These advancements are making cloud AI an essential component of modern business strategies.
Large organizations typically operate multiple business units, each managing distinct lines of business (LOBs) under a central governing entity. To streamline operations, enterprises commonly adopt AWS Organizations with a multi-account strategy. This approach allows them to implement cloud AI solutions while maintaining centralized governance, identity management, security guardrails, and policy enforcement.
As the adoption of cloud AI grows, enterprises must establish a well-defined operating model. An effective cloud AI operating model aligns organizational design, core processes, governance structures, and financial planning to optimize AI-driven business operations.
In this post, we explore different cloud AI operating model architectures that enterprises can adopt for successful implementation.
Cloud AI Operating Model Patterns
Enterprises can adopt different cloud AI operating models based on their priorities around agility, governance, and centralized control. Governance in the context of cloud AI refers to the frameworks, policies, and processes that ensure responsible development, deployment, and ethical usage of AI technologies. Three common operating model patterns are:
1. Decentralized Model
2. Centralized Model
3. Federated Model
Decentralized Cloud AI Model
In a decentralized approach, generative AI development and deployment are initiated and managed by the individual LOBs themselves. LOBs have autonomy over their AI workflows, models, and data within their respective AWS accounts.
This enables faster time-to-market and agility because LOBs can rapidly experiment and roll out generative AI solutions tailored to their needs. However, even in a decentralized model, often LOBs must align with central governance controls and obtain approvals from the CCoE team for production deployment, adhering to global enterprise standards for areas such as access policies, model risk management, data privacy, and compliance posture, which can introduce governance complexities.
Centralized Cloud AI Model
In a centralized operating model, all generative AI activities go through a central generative artificial intelligence and machine learning (AI/ML) team that provisions and manages end-to-end AI workflows, models, and data across the enterprise.
LOBs interact with the central team for their AI needs, trading off agility and potentially increased time-to-market for stronger top-down governance. A centralized model may introduce bottlenecks that slow down time-to-market, so organizations need to adequately resource the team with sufficient personnel and automated processes to meet the demand from various LOBs efficiently. Failure to scale the team can negate the governance benefits of a centralized approach.
Federated Cloud AI Model
A federated model strikes a balance by having key activities of the generative AI processes managed by a central generative AI/ML platform team.
While LOBs drive their AI use cases, the central team governs guardrails, model risk management, data privacy, and compliance posture. This enables agile LOB innovation while providing centralized oversight on governance areas.
Cloud AI Architecture Components
Before diving deeper into these operating models, let’s review key cloud AI architecture components that support enterprise AI adoption.
Large Language Models in Cloud AI
Large Language Models (LLMs) are at the core of cloud AI applications. These models power generative AI solutions but may produce inaccurate or outdated responses. One effective strategy to mitigate inaccuracies is Retrieval-Augmented Generation (RAG), which enhances LLMs with real-time knowledge retrieval.
Amazon Bedrock and Amazon SageMaker JumpStart provide access to foundation models (FMs) and tools that enable enterprises to integrate cloud AI solutions with robust governance and security.
Data Sources, Embeddings, and Vector Stores
Enterprises store domain-specific data across various platforms, including data lakes, document repositories, and structured databases. Cloud AI applications use vector stores to store and retrieve relevant data for LLM processing. Amazon Bedrock Knowledge Bases simplifies this process, enabling enterprises to implement Retrieval-Augmented Generation seamlessly.
Guardrails for Secure Cloud AI
To ensure responsible AI usage, cloud AI solutions require robust safeguards. Content filtering, redaction of personally identifiable information (PII), and compliance enforcement are essential to prevent misuse.
Amazon Bedrock Guardrails offers policy-based safeguards, allowing organizations to tailor content moderation according to their governance standards.
Operating Model Architectures
This section provides an overview of the three kinds of operating models.
Decentralized Operating Model
In a decentralized operating model, LOB teams maintain control and ownership of their AWS accounts. Each LOB configures and orchestrates generative AI components, common functionalities, applications, and Amazon Bedrock configurations within their respective AWS accounts. This model empowers LOBs to tailor their generative AI solutions according to their specific requirements.
With this model, the LOBs configure the core components, such as LLMs and guardrails, and the Amazon Bedrock service account manages the hosting, execution, and provisioning of interface endpoints. These endpoints enable LOBs to access and interact with the Amazon Bedrock services they’ve configured.
Each LOB performs monitoring and auditing of their configured Amazon Bedrock services within their account, using Amazon CloudWatch Logs and AWS CloudTrail for log capture, analysis, and auditing tailored to their needs. Amazon Bedrock cost and usage will be recorded in each LOB’s AWS accounts. By adopting this decentralized model, LOBs retain control over their generative AI solutions through a decentralized configuration while benefiting from the scalability, reliability, and security of Amazon Bedrock.
Centralized Operating Model
The centralized AWS account serves as the primary hub for configuring and managing the core generative AI functionalities, including reusable agents, prompt flows, and shared libraries. LOB teams contribute their business-specific requirements and use cases to the centralized team, which then integrates and orchestrates the appropriate generative AI components within the centralized account.
Although the orchestration and configuration of generative AI solutions reside in the centralized account, they often require interaction with LOB-specific resources and services. To facilitate this, the centralized account uses API gateways or other integration points provided by the LOBs’ AWS accounts. These integration points enable secure and controlled communication between the centralized generative AI orchestration and the LOBs’ business-specific applications, data sources, or services. This centralized operating model promotes consistency, governance, and scalability of generative AI solutions across the organization.
The centralized team maintains adherence to common standards, best practices, and organizational policies while also enabling efficient sharing and reuse of generative AI components. Furthermore, the core components of Amazon Bedrock, such as LLMs and guardrails, continue to be hosted and executed by AWS in the Amazon Bedrock service account, promoting secure, scalable, and high-performance execution environments for these critical components.
In this centralized model, monitoring and auditing of Amazon Bedrock can be achieved within the centralized account, allowing for comprehensive monitoring, auditing, and analysis of all generative AI activities and configurations. Amazon CloudWatch Logs provides a unified view of generative AI operations across the organization.
Federated Operating Model
In a federated model, Amazon Bedrock enables a collaborative approach where LOB teams can develop and contribute common generative AI functionalities within their respective AWS accounts. These common functionalities, such as reusable agents, prompt flows, or shared libraries, can then be migrated to a centralized AWS account managed by a dedicated team or CCoE.
The centralized AWS account acts as a hub for integrating and orchestrating these common generative AI components, providing a unified platform for action groups and prompt flows. Although the orchestration and configuration of generative AI solutions remain within the LOBs’ AWS accounts, they can use the centralized Amazon Bedrock agents, prompt flows, and other shared components defined in the centralized account.
This federated model allows LOBs to retain control over their generative AI solutions, tailoring them to specific business requirements while benefiting from the reusable and centrally managed components. The centralized account maintains consistency, governance, and scalability of these shared generative AI components, promoting collaboration and standardization across the organization.
Organizations frequently prefer storing sensitive data, including Payment Card Industry (PCI), personally identifiable information (PII), General Data Protection Regulation (GDPR), and Health Insurance Portability and Accountability Act (HIPAA) information within their respective LOB AWS accounts. This approach makes sure that LOBs maintain control over their sensitive business data in the vector store while preventing centralized teams from accessing it without proper governance and security measures.
A federated model combines decentralized development, centralized integration, and centralized monitoring. This operating model fosters collaboration, reusability, and standardization while empowering LOBs to retain control over their generative AI solutions.
Cost Management
Organizations may want to analyze Amazon Bedrock usage and costs per LOB. To track the cost and usage of FMs across LOBs’ AWS accounts, solutions that record model invocations per LOB can be implemented.
Amazon Bedrock now supports model invocation resources that use inference profiles. Inference profiles can be defined to track Amazon Bedrock usage metrics, monitor model invocation requests, or route model invocation requests to multiple AWS Regions for increased throughput.
There are two types of inference profiles. Cross-Region inference profiles, which are predefined in Amazon Bedrock and include multiple AWS Regions to which requests for a model can be routed. The other is application inference profiles, which are user created to track cost and model usage when submitting on-demand model invocation requests. You can attach custom tags, such as cost allocation tags, to your application inference profiles. When submitting a prompt, you can include an inference profile ID or its Amazon Resource Name (ARN). This capability enables organizations to track and monitor costs for various LOBs, cost centers, or applications.
Conclusion
Enterprises often begin with a centralized cloud AI model but transition to a federated cloud AI approach as AI adoption scales. A federated model enables LOBs to experiment with cloud AI while ensuring enterprise-wide governance, compliance, and security.
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Implement Cloud AI with Cloudastra
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If your organization is looking to optimize its cloud AI strategy, Cloudastra provides the tools and expertise needed to build scalable, secure, and high-performing AI applications.
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