1. Model Selection
Choose the right model by balancing quality, latency, cost, size, hardware fit, and deployment needs.
Methodology
The InferenceOps Methodology
InferenceOps is the operational discipline of running Generative AI inference in production. It is not limited to deploying a model endpoint. It covers the broader system of choices, controls, measurements, and improvements required to make inference successful at scale.
The methodology organizes production inference into a coherent operating model to guide design, deployment, governance, observability, and continuous improvement.
Core Areas of InferenceOps
2. Model Evaluation
Test models on realistic tasks and operational conditions, not only generic benchmark scores.
3. Capacity Planning
Estimate infrastructure, GPU requirements, concurrency limits, throughput needs, and scaling behavior.
4. Model Deployment
Package, serve, and expose models with operational reliability and repeatability.
5. Deployment Optimization
Improve efficiency through batching, quantization, tuning, memory strategies, and serving configuration.
6. Long-Term Memory Management
Design retention and retrieval strategies, including memory architecture and retrieval patterns.
7. Authentication and Audit
Build traceability, identity controls, access policies, and operational accountability.
8. Guardrails
Apply policy controls, validation, filtering, safety checks, and output protections.
9. Logging
Capture operational and application-level signals for debugging, compliance, and optimization.
10. Monitoring
Track health, utilization, latency, error rates, request behavior, and service performance.
11. Observability
Use metrics, traces, logs, dashboards, and diagnostics to understand complex inference behavior.
12. Semantic Routing
Route requests intelligently by cost, complexity, model specialization, or policy requirements.
13. Cost Per Token Optimization
Treat unit economics as a core concern and continuously improve cost-performance balance.
14. Continuous Improvement
Use telemetry, incidents, and workload feedback to improve systems over time.
15. Batch vs. Live Inference
Choose synchronous or asynchronous patterns based on workload and operational objectives.