Minimum Viable Architecture

Prompt:

You are a platform architect. 

Propose a Minimum Viable Architecture for an Enterprise AI Platform, documented using C4 Model diagrams in a format that can be rendered by Mermaid. 

A Minimum Viable Architecture (MVA) is defined as the architecture that satisfies Functional and Quality Requirements for the Minimum Viable Platform and can also be extended (without the need for re-factoring or re-platforming it) to support the known future Quality Requirements. 

Minimum Viable Platform Requirements: 

• Security and governance Compliance: 

  • Ensure the platform complies with relevant regulations like GDPR, HIPAA, or SOC 2. 

  • Auditing: Maintain detailed audit logs for regulatory reporting and tracking. 

  • Data sovereignty: Support data residency requirements to control where data is stored and processed. 

• Data management and integration 

  • Data pipelines: Support both batch and streaming data processing pipelines. 

  • Data catalog: Provide a data catalog to help data scientists discover and access the data they need.

• Scalability and performance 

  • Scalability: The infrastructure must be able to scale both horizontally and vertically to handle growing data volumes and expanding AI use cases. 

  • Performance: The platform should efficiently handle large-scale model training and real-time inference workloads. 

• Model development and lifecycle 

  • Integrated development: Support the entire AI algorithm development lifecycle, from design and testing to deployment. 

  • Multi-model support: The platform should accommodate multiple models, including large language models (LLMs) and custom-built neural networks. 

• Operationalization and integration

  • APIs: Provide APIs to trigger predictions and re-training programmatically. 

  • Existing infrastructure: Seamlessly integrate into existing IT infrastructure without requiring a complete rip-and- replace of legacy systems. 

  • Developer experience: Offer tools like SDKs and connectors to integrate with other enterprise tools and workflows 

  • Uptime must exceed 99%. 

  • Compliance with SOC 2 and GDPR is required.

Known future Quality Requirements: 

• Security and governance 

  • Data security: Implement strong encryption for data both at rest and in transit. 

  • Access control: Use role-based access control to manage user permissions and limit access to sensitive data. 

• Data management and integration 

  • Data integration: Ability to ingest and integrate data from various sources, including structured and unstructured data from ERP, CRM, and cloud storage. 

  • Centralized governance: Implement mechanisms for centralized data governance to regulate access and support risk management. 

• Model development and lifecycle Integrated development: 

  • Support the entire AI algorithm development lifecycle, from design and testing to deployment. 

  • Multi-model support: The platform should accommodate multiple models, including large language models (LLMs) and custom-built neural networks. 

• Able to support 10000users, with 10% being concurrently logged on at peak time 

• Uptime must exceed 99.9%.