MLOps Pipeline Diagram
Trace the flow from training and CI/CD to deployment, monitoring, and retraining.
Free to start · Fully editable · Export to SVG, PNG, GIF & MP4
What's in this template
7 connected components you can rename, recolor, and extend with AI.
An MLOps pipeline diagram shows how machine learning models move from development into reliable production operation. It centers on an automation orchestrator linking data and model versioning, CI/CD, a model registry, deployment to a serving layer, production monitoring, and a feedback loop that triggers automated retraining when drift is detected.
ML engineers, platform teams, and DevOps practitioners use this MLOps diagram to standardize model delivery, govern releases, and keep models accurate over time. It is a strong reference for designing an MLOps pipeline, planning CI/CD for ML, or explaining continuous training to stakeholders.
Great for
- MLOps platform design
- CI/CD for ML planning
- Model governance reviews
- Continuous training docs
- Engineering onboarding
Frequently asked questions
What is an MLOps pipeline?+
It is an automated workflow that takes machine learning models from training through CI/CD, deployment, and monitoring, with a feedback loop that retrains models as data changes.
What are the components of MLOps?+
Core components include data and model versioning, CI/CD automation, a model registry, a serving layer, production monitoring with drift detection, and an automated retraining loop.
How is MLOps different from a training pipeline?+
A training pipeline produces a model from data, while MLOps wraps deployment, monitoring, governance, and continuous retraining around that model to keep it reliable in production.
What is model drift monitoring?+
It tracks how production data and predictions diverge from training conditions, alerting teams or triggering retraining when accuracy degrades due to changing data.
Related templates
View all AI & ML →How LLMs Work
A clear map of how large language models turn a prompt into an answer — and the models and hardware behind them
Claude Code Architecture
How Claude Code reads your repository, calls tools through MCP, and edits code from the terminal
Codex Architecture
How an AI coding agent like Codex plans, writes and tests code inside a secure sandbox
AI Tool Ecosystem
Your AI stack as a living orbit — models, frameworks and vector stores revolving around the product they power
RAG Architecture Diagram
Map how retrieval-augmented generation grounds an LLM in your data with a vector database
AI Agent Architecture Diagram
Visualize the reasoning loop, tools, and memory that let an AI agent plan and act
ML Training Pipeline Diagram
Chart every stage from raw data to a trained, validated machine learning model
LLM Application Architecture Diagram
See how a production LLM app wires frontend, orchestration, model APIs, and guardrails
Make it yours in seconds
Open the mlops pipeline diagram in the Infogiph canvas, then edit, animate, and export.
Use this template