Pipeline

A pipeline is a series of connected processing stages where data flows through in sequence, with each stage transforming the data for the next. Pipelines bring structure and reliability to complex AI operations.

Types of AI pipelines:

• Data pipelines: Extract, transform, load (ETL) operations
• ML pipelines: Training, validation, deployment workflows
• Inference pipelines: Processing requests through multiple steps
• RAG pipelines: Query → retrieve → augment → generate

Pipeline components:

• Sources: Where data originates
• Transforms: Operations that modify data
• Sinks: Where results are stored/sent
• Monitors: Track pipeline health and quality

Benefits of pipeline architecture:

• Modularity: Easy to modify individual steps
• Testability: Each stage can be tested independently
• Reusability: Components can be shared across pipelines
• Observability: Clear points to monitor and debug
• Scalability: Individual stages can be scaled independently

Pipeline best practices:

• Design for idempotency (rerunning produces same result)
• Implement comprehensive logging
• Build in data validation between stages
• Plan for failure recovery