AI & ML Systems
Architecture patterns for building, deploying, monitoring, and governing machine learning and AI systems in production.
ML System Design
Designing end-to-end machine learning systems from data ingestion to model serving.
ML System Architecture
Reference architecture for scalable machine learning platforms.
Training Pipelines
Orchestrating data preparation, model training, evaluation, and registration.
Model Training at Scale
Distributed training strategies, hyperparameter tuning, and experiment tracking.
Model Serving
Real-time and batch inference infrastructure — REST, gRPC, and streaming serving.
Model Monitoring
Data drift, concept drift, and performance monitoring for deployed ML models.
MLOps
Applying DevOps practices to ML — CI/CD for models, reproducibility, and governance.
RAG Architecture
Retrieval-Augmented Generation — vector search, chunking, and LLM integration.
Vector Databases
Pinecone, Weaviate, Qdrant, and pgvector for semantic search and RAG.
LLM Architecture
Foundation models, fine-tuning, prompt engineering, and deployment patterns.
Feature Stores
Centralized feature management for training-serving consistency.
AI Guardrails
Safety controls, content filtering, PII detection, and output validation for LLM systems.
AI Observability
Tracing LLM calls, prompt/response logging, latency, and cost monitoring.
Prompt Engineering
Patterns for effective prompting — few-shot, chain-of-thought, and structured outputs.