Nothing close enough? Start from a blank block diagram → Describe it in one paragraph.
How to use a block diagram template.
- 01Describe the training pipeline
Start with data flow: "raw data in S3 → Spark ETL → feature store → model training on SageMaker → model registry".
- 02Add the inference path
Describe how the model is served: "real-time REST endpoint via FastAPI on Kubernetes with a feature store lookup for each request".
- 03Include monitoring and retraining
Ask to "add a model monitoring block that detects drift and triggers an automated retraining job".
- 04Show experiment tracking
Mention MLflow, Weights & Biases, or Neptune and the AI adds the experiment tracker as a side component reading from the training block.
- 05Export for design docs
Download as PNG or SVG to include in system design documents, research papers, or internal ML platform wikis.
Questions about block diagram templates
What is the difference between a training pipeline and an inference pipeline?
A training pipeline transforms raw data into a trained model artifact (data → features → training → evaluation → registry). An inference pipeline takes a new input, retrieves features, runs the model, and returns a prediction. Both share feature engineering logic but run at different frequencies and latency requirements.
Can I draw a real-time vs batch inference architecture comparison?
Yes. Ask for "two inference architectures side by side: real-time REST serving and nightly batch scoring" and ChatDiagram generates both as separate blocks in the same diagram.
How do I show a feature store in the architecture?
Describe which features come from the offline store (batch training) and which from the online store (low-latency inference). The AI draws the feature store with both offline and online paths.
Is this suitable for LLM / generative AI architectures?
Yes. Describe the components — base LLM, RAG pipeline, vector database, prompt template, guardrails, evaluation — and ChatDiagram draws the generative AI system architecture.