ML Pipeline Architecture Flow¶
Scaffold
This MicroSim has been scaffolded from its specification. The interactive implementation has not been built yet.
Learning Objective¶
TBD
- Bloom Level: TBD
- Bloom Verb: TBD
- Library: vis-network
Preview¶
Specification¶
The full specification below is extracted from Chapter 17: AI and Machine Learning System Architecture.
Type: Interactive workflow diagram
**sim-id:** ml-pipeline-explorer<br/>
**Library:** vis-network<br/>
**Status:** Specified
**Purpose:** Interactive visualization of the complete ML pipeline from data ingestion through deployment, with quality attribute risk indicators at each stage and ATAM evaluation hooks.
**Nodes:** Data Sources, Data Ingestion, Feature Engineering, Feature Store, Model Training, Model Registry, Model Evaluation, Deployment (Batch/Online/Streaming), Monitoring
**Interactions:**
- Click each node to see: quality attribute risks, common failure modes, ATAM sensitivity points
- Toggle pipeline type (batch, online, streaming) to see architecture variations
- "Simulate Drift" button shows monitoring alerts activating
**Display:** Risk heat map overlay showing which pipeline stages are most commonly flagged in ATAM evaluations.