AI Platform Architectural Considerations
Data Management
- Data Lakehouse: Centralized repository for storing and managing large volumes of structured and unstructured data, enabling efficient data processing and analytics.
Model Management
- Embeddings Model: Generates vector representations of data, facilitating efficient similarity searches and information retrieval.
- Embeddings DB: Stores and indexes vector embeddings for quick and scalable similarity searches.
Orchestration and Routing
- Orchestration Routing: Manages the flow of requests and responses between different components of the AI system, ensuring efficient resource utilization and scalability.
Security and Access Control
- Hybrid Identity Service: Manages user authentication and authorization across on-premises and cloud environments, ensuring secure access to AI resources.
API and Plugin Management
- APIs/Plugins: Provides interfaces for external systems to interact with the AI platform, enabling integration and extensibility.
Development and Operations
- DevSecOps: Integrates security practices into the development and operations processes, ensuring continuous security throughout the AI platform lifecycle.
Model Deployment and Serving
- Guardrails Service: Implements safety and security measures to control input and output of AI models, preventing misuse and ensuring compliance with ethical guidelines.
Caching and Performance Optimization
- Cache: Improves response times and reduces computational load by storing frequently accessed data or model outputs.
Synthetic Data Generation
- Model Factory Synthetic Data Pipeline: Generates artificial data for training and testing AI models, addressing data scarcity and privacy concerns.
Local Execution
- Local Orchestration/Router: Enables AI inference and orchestration on local devices or edge environments, reducing latency and supporting offline capabilities.
In-Memory Processing
- In-mem Databases: Utilizes memory-based data storage for ultra-fast data access and processing, crucial for real-time AI applications.
Lifecycle Management
- Lifecycle / Control Plane Agent: Manages the entire lifecycle of AI models and services, from deployment to monitoring and updates.
Specialized Backend Services
- Base Inferencing Backend: Provides core inferencing capabilities, possibly leveraging KamiwazaAI for distributed, scalable inferencing.
- Function Calling Backend: Enables AI models to interact with and invoke external functions or services, expanding their capabilities.
- Coding Backend: Supports code generation and execution capabilities for AI models.
Resource Management
- Backend Inferencing and APIs on GPU, CPU, IPU, NPU: Optimizes AI workloads across various hardware accelerators, ensuring efficient utilization of computational resources.
By considering these elements in your AI platform architecture, you can build a robust, scalable, and secure system that meets your specific needs while leveraging the strengths of KamiwazaAI for core inferencing capabilities.
Component Implementation Examples
Below is a table showing potential implementations for various components of the AI platform:
| Component | Implementation Examples |
|---|---|
| Data Lakehouse | TBD |
| Data Retrieval | KamiwazaAI |
| Embeddings Model | Stella_en_1.5B_v5, BGE-EN-ICL |
| Embeddings DB | KamiwazaAI + Milvus (More options coming, e.g.Qdrant) |
| Embeddings Engine | KamiwazaAI |
| Orchestration Routing | DSPy |
| Hybrid Identity Service | AAD-DS? |
| APIs/Plugins | OpenAPI, gRPC |
| DevSecOps | ____ |
| Guardrails Services | DSPy, Guardrails AI, NeMo Guardrails |
| Cache | Valkey, Redis, etcd, etc |
| Execution Orchestration/Router | DSPy, AICI |
| Lifecycle / Control Plane Agent | Rust-based custom solution |
| Base Inferencing Backend | Llama 3 (to be 3.1) |
| Function Calling Backend | Llama 3 (to be 3.1) |
| Coding Backend | DeepSeek-Coder-v2, Llama 3 (to be 3.1) |
| Backend API Services | FastAPI, Integration Hub |
| Backend Inferencing and APIs | KamiwazaAI |
| Model Factory Synthetic Data Pipeline | DSPy, Gretel.ai** |
This table provides examples of specific technologies or solutions that could be used to implement each component of the AI platform.
** Gretel.ai did transition to a "SAL" style license from open source, but at last check we believe still freely usable for commercial purposes internal to an organization.