🧠 AI & Machine Learning
AI & Machine Learning architecture diagram templates
RAG chatbots, AI agents, model training and serving, LLM gateways and MLOps pipelines. Open any template to see the finished diagram, then use it as the starting point for your own in the Studio.
11 templates
RAG Chatbot over Company Documents
A chatbot that answers staff questions from company documents: documents are split and indexed in a vector database, and each question pulls the most relevant passages before the language model writes an answer.
RAG Question Answering Sequence
What happens when a user asks the document chatbot a question: permission check, embedding, vector search, prompt building and a cited answer.
Document Ingestion for Vector Search
How documents become searchable passages for an AI assistant: extraction, cleaning, chunking, embedding and indexing, with changed files re-processed automatically.
AI Agent Tool-Use Workflow
How an AI agent completes a task by planning, calling tools, checking results and asking a person to approve risky actions before it finishes.
MLOps Model Training Pipeline
Where each part of an MLOps setup runs: feature store, training jobs on GPU nodes, experiment tracking, model registry and automated deployment to serving.
ML Model Lifecycle
The states a machine learning model moves through, from experiment to production to retirement, including rollback when live performance drops.
LLM Gateway with Cost and Safety Controls
One gateway that every app in a company uses to reach language models: it applies budgets, rate limits, content filters and caching, and records usage per team.
ML Experiment Tracking Schema
Tables that keep machine learning work reproducible: datasets and their versions, experiments, runs, metrics, and registered models with their deployments.
Real-time Model Prediction Sequence
How an app gets a live prediction: features are fetched from an online store, the model scores the request, and the result and inputs are logged for monitoring.
GPU Inference Cluster Deployment
How a self-hosted language model is deployed for many users: autoscaled GPU pods behind a gateway, with model weights cached close to the GPUs.
Generative AI Use Cases in a Company
Where generative AI is used across a typical company, grouped by department, to help plan which projects to start first.
More AI & Machine Learning templates are being added regularly.