Data Pipeline
An automated workflow that extracts, transforms, and loads data from various sources into formats suitable for AI model training and inference.
In Depth
A data pipeline is an automated series of processing steps that move and transform data from source systems into formats required by downstream consumers, particularly AI and machine learning systems. In the context of AI applications, data pipelines handle the critical work of ingesting raw data, cleaning and validating it, applying transformations and feature engineering, and delivering processed data to training systems, feature stores, vector databases, and other components of the ML infrastructure.
AI data pipelines encompass several specialized workflow types. Training data pipelines prepare and deliver curated datasets for model training, including data collection, deduplication, quality filtering, format conversion, and train/validation/test splitting. RAG ingestion pipelines process documents through chunking, embedding generation, metadata extraction, and vector database indexing. Feature pipelines compute ML features from raw data sources and deliver them to feature stores. Evaluation pipelines generate and process test datasets for model assessment.
Modern data pipeline architectures support both batch processing for large-scale data preparation and stream processing for real-time data needs. Apache Spark, Databricks, and cloud-native services handle batch workloads. Apache Kafka, Apache Flink, and cloud streaming services process real-time data flows. Orchestration tools like Apache Airflow, Prefect, and Dagster manage pipeline scheduling, dependency resolution, retry logic, and monitoring. Infrastructure-as-code approaches ensure pipeline reproducibility and version control.
Production data pipelines for AI systems must address data quality monitoring (detecting anomalies, schema changes, and distribution shifts), lineage tracking (understanding the provenance of every data point), access control and privacy compliance (managing PII and sensitive data), scalability (handling growing data volumes without manual intervention), and observability (understanding pipeline health, throughput, and latency). Robust data pipelines are often the difference between AI systems that work reliably in production and those that degrade unpredictably.
Related Terms
Training Data
The curated dataset used to train or fine-tune machine learning models, directly determining model capabilities, biases, and limitations.
Feature Store
A centralized platform for managing, storing, and serving machine learning features consistently across training and inference pipelines.
Data Labeling
The process of annotating raw data with informative tags or labels that enable supervised machine learning models to learn from examples.
MLOps
A set of practices combining machine learning, DevOps, and data engineering to reliably deploy and maintain ML models in production.
Machine Learning
A branch of artificial intelligence where systems learn patterns from data to make predictions or decisions without being explicitly programmed for each scenario.
Related Services
Cloud AI Modernisation
Refactoring AWS, Azure, GCP, and Oracle workloads into production-grade AI stacks. Multi-cloud RAG pipelines, observability, guardrails, and MLOps that slot into existing engineering rhythms.
Data Flywheel Operations
Standing up the flywheel: telemetry, preference signals, human feedback loops, and automated re-training that can unlock up to 98.6% inference cost reduction without losing accuracy targets.
Custom Model Training & Distillation
Training domain models on curated corpora, applying NeMo and LoRA distillation, and wiring evaluation harnesses so accuracy stays high while latency and spend drop.
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