Welcome to Ai Architecture Views Architecture Diagrams

Browse detailed system architecture diagrams, covering patterns, communication models, deployments, and network designs. Ideal for learning software system design visually.

Available Diagrams

End-to-End Machine Learning SystemIllustrates a complete ML system pipeline: data ingestion (Kafka, APIs), feature engineering, model training (using frameworks like TensorFlow or PyTorch), model registry, CI/CD deployment, and live model monitoring.
Large Language Model (LLM) Chatbot ArchitectureShows how a chatbot uses an LLM (e.g., GPT) with layers for user input, context management, prompt templating, LLM API access, and a feedback loop for reinforcement learning.
Retrieval-Augmented Generation (RAG) ArchitectureDescribes an LLM architecture enhanced with external data sources. Includes embedding generation, vector database (e.g., Pinecone), semantic search, and response synthesis by the LLM.
AI Model Deployment with MLOpsOutlines CI/CD pipelines for AI models, covering training-to-deployment flows using MLflow, Docker, Kubernetes, model registries, and performance monitoring tools.
Edge AI Inference PipelineShows how AI models are optimized (e.g., TensorRT, ONNX) and deployed to edge devices (Jetson Nano, Coral TPU) for real-time inference, including cloud sync and local decision logic.
Generative AI Training WorkflowDepicts the data and compute pipeline for training generative models (LLMs, diffusion models). Includes data preprocessing, distributed training (TPUs/GPUs), checkpointing, and evaluation.
Multi-Modal AI System DesignRepresents a system combining multiple AI models (e.g., vision + NLP). Each model runs independently, feeding into a unified decision layer for applications like autonomous systems or smart assistants.
AI-Powered Recommendation EngineHighlights architecture of a recommendation engine using user interaction logs, collaborative filtering, matrix factorization, and real-time model inference for personalization.
Federated Learning SystemExplains federated learning setup where multiple client devices train model updates locally and sync with a central server, ensuring data privacy and decentralization.
Explainable AI (XAI) ArchitectureDescribes how XAI models integrate with interpreters (e.g., SHAP, LIME) to provide transparent decision-making, including UI components for visualizing feature importance and predictions.