Architecture Blueprints
Visual reference architectures for real-world AI systems.
LLM Inference Runtime
Complete architecture for serving LLMs with batching, caching, and load balancing.
KV Cache Architecture
Memory management and optimization strategies for attention KV caching.
Continuous Batching System
Dynamic batching strategies for maximizing GPU utilization in inference.
Agentic AI Stack
Multi-agent systems with tool calling, memory, and orchestration layers.
RAG Production Architecture
Production-grade retrieval augmented generation with hybrid search.
LLM Evaluation Pipeline
Automated evaluation frameworks for model quality and regression testing.
ML Feature Platform
Feature engineering, storage, and serving infrastructure for ML systems.
Model Training Platform
Scalable training infrastructure with experiment tracking and versioning.
Model Serving Platform
Multi-model serving with A/B testing, canary deployments, and monitoring.
Distributed Training on Kubernetes
Kubernetes-native distributed training with autoscaling and fault tolerance.
Multi-Tenant LLM Serving Platform
Shared LLM infrastructure with isolation, rate limiting, and cost allocation.
Production ML Monitoring Architecture
Observability stack for ML systems with drift detection and alerting.