Data Pipeline
An automated series of steps that collect, process, transform, and deliver data from source systems to where it's needed for AI model training or inference.
Components
A typical ML data pipeline includes: data ingestion (APIs, databases, files), cleaning (handling missing values, deduplication), transformation (feature engineering, normalization), validation (schema checks, quality tests), and storage (data lakes, feature stores).
Tools
Apache Airflow and Prefect for orchestration. dbt for transformation. Great Expectations for data quality. Apache Spark for large-scale processing. Feature stores like Feast for serving features to models.
Why It Matters
Data quality issues are the #1 cause of ML project failures. A robust data pipeline ensures consistent, reliable data reaches your models. The principle 'garbage in, garbage out' is especially true in ML.