01Pipeline purpose, lifecycle, outputs, and production boundary.
AI Data Pipeline OrchestratorDATA & MLOPS PIPELINE WORKSPACEA visual planning and simulation workspace for defining ingestion, validation, cleaning, feature engineering, training, evaluation, and deployment stages.
Model the full data-to-model lifecycle as explicit stages with dependencies, policies, retries, state, and measurable quality gates.
A visual planning and simulation workspace for defining ingestion, validation, cleaning, feature engineering, training, evaluation, and deployment stages.
Stage configurations, execution simulations, telemetry, quality-gate results, logs, and workflow JSON.
The browser demonstrates orchestration logic. Production runs still require storage, compute, secrets, scheduling, and a durable execution backend.
Every stage is separated so the interface stays readable, reviewable, and useful for AI and Data Science learning.
Select stages and configure dependencies, timeouts, and quality policies.
Simulate the workflow with deterministic stage transitions.
Inspect latency, retries, logs, and gate outcomes.
Save a reusable workflow definition for implementation planning.
