tangle-pipeline-best-practices

name	Tangle Pipeline Best Practices
description	Guidelines for building well-structured, maintainable ML pipelines in Tangle

Tangle Pipeline Best Practices

Pipeline Structure

1. Logical Stages: Organize pipelines into clear stages — data ingestion, preprocessing, training, evaluation, deployment.
2. Subgraph Grouping: Group related tasks into subgraphs. Each subgraph should represent one logical stage.
3. Size Limits: Keep subgraphs under 7 nodes. If a subgraph grows larger, split it.
4. Naming: Use descriptive, human-readable names for tasks. "Preprocess Training Data" not "preprocess_1".

Component Selection

1. Search First: Always check the component registry before creating new components.
2. Single Responsibility: Prefer components that do one thing well over monolithic ones.
3. Type Compatibility: Verify input/output types match when connecting components.
4. Reuse: If a component exists that's close to what you need, prefer it over creating a new one.

Data Flow

1. No Cycles: Pipeline graphs must be directed acyclic graphs (DAGs).
2. Explicit Connections: Always use bindings to connect data flow — avoid implicit dependencies.
3. Pipeline Inputs: Use pipeline-level inputs for configurable parameters that should be settable at run time.
4. Pipeline Outputs: Define pipeline-level outputs for results that need to be accessible after the run.

Common Patterns

• Fan-out: One source feeding multiple processors (e.g., train/test split).
• Fan-in: Multiple sources feeding into one aggregator.
• Chain: Linear sequence of transforms (most common).
• Conditional: Use task-level isEnabled for optional processing steps.