AIRE is an open-source framework that applies Site Reliability Engineering (SRE) principles and practices to AI systems. It provides a comprehensive toolkit and methodology for AI/ML practitioners, Data Engineers, DevOps, and SRE teams to develop, deliver, and operate reliable AI products.
As AI becomes critical for business competitiveness, organizations face unique challenges in integrating these systems reliably and securely. AIRE bridges this gap by combining CNCF ecosystem tools with established SRE practices to enhance AI system reliability, security, and business alignment.
AI systems present unique operational challenges that traditional SRE practices don't fully address:
- Limited Visibility: Lack of control over AI lifecycle including data collection, model deployment, and monitoring
- Quality Assurance: Ensuring model robustness against prompt attacks, data drift, and performance degradation
- Complexity Management: Handling intricate dependencies, configurations, and resources across environments
- Security Concerns: Protecting against data leakage, bias, and malicious attacks
- Operational Integration: Incorporating AI systems into existing DevOps workflows
AIRE addresses these challenges by providing:
- A structured approach to AI system reliability
- Tools and practices for managing AI-specific risks
- Methods for defining and measuring AI system reliability
- Integration patterns for existing MLOps and DevOps workflows
- Standardized processes for AI operations
- Guidelines and best practices for AI reliability
- Templates and checklists for implementation
- Reference architectures integrating CNCF tools
- Implementation examples with real-world scenarios
- Documentation standards for AI systems
- Open-source tools collection
- AI-specific monitoring and observability solutions
- Testing and validation utilities for LLMs
- Deployment templates for AI workflows
- Security scanning and prompt attack prevention tools
- Language chain tracing capabilities
- AI gateway integration patterns
Based on the Flux OCI architecture, AIRE provides a streamlined approach for deploying LLMs to Kubernetes:
- GitOps-driven Deployment: Utilize custom controllers to manage LLM deployments through Git workflows
- Infrastructure as Code: Define LLM configurations, resource requirements, and scaling policies declaratively
- Automated Rollouts: Support for canary deployments and automated rollbacks
- Resource Optimization: Intelligent scheduling and resource management for GPU/CPU workloads
- Model Versioning: Integrated version control and model artifact management
- Standardized OCI Image Format: Ensure consistency by unifying LLM deployments around the OCI image format
Leveraging OpenInference for comprehensive LLM observability:
- Distributed Tracing: End-to-end visibility into LLM request flows and chain-of-thought processes
- Performance Metrics: Track latency, throughput, and resource utilization
- Semantic Logging: Structured logging for prompt engineering and response analysis
- Cost Monitoring: Track token usage and associated costs
- Quality Metrics: Monitor hallucination rates, response quality, and model drift
Following industry best practices for AI gateway implementation:
- Traffic Management: Rate limiting, load balancing, and request routing
- Security Controls: Authentication, authorization, and prompt validation
- Cost Optimization: Smart caching and request batching
- Model Governance: Version control, A/B testing, and shadow deployment
- API Standardization: Unified interface for multiple LLM providers
- Reliability: Define and track SLOs/SLIs specific to AI systems
- Operations: Streamlined maintenance and monitoring
- Development: Faster and safer deployment cycles
- Collaboration: Better alignment between AI/ML and SRE teams
- Risk Management: Reduced operational and compliance risks
- Standards Promotion: Framework for ensuring reliability in AI workflows
- Technology Bridge: Adaptation of CNCF tools for AI-specific challenges
- Enhanced Observability: Practical implementations for AI lifecycle monitoring
- Ethical AI: Methods to reduce risks and ensure compliance
- Operational Excellence: Standardized processes for AI integration
[Coming Soon]
[Coming Soon]
We welcome contributions! Please see our Contributing Guidelines for details.
- YouTube
- [Discord/Slack Channel]
- [Discussion Forum]
- [Community Meetings]
- ENTERPRISE GENAI DELIVERY PATTERNS
- FMOps/LLMOps on AWS
- OpenAI: Scaling Kubernetes to 7,500 nodes
- GPT-4 Architecture Overview
- Ray Framework
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.