What is AI Pipeline?
TL;DR
An automated workflow covering the entire AI process from data collection through model training, inference, and deployment.
AI Pipeline: Definition & Explanation
An AI Pipeline is an automated, connected workflow covering the entire sequence of AI/ML processing — from data collection, preprocessing, and feature engineering to model training, evaluation, inference, and deployment. By defining these steps as a pipeline rather than executing them manually, reproducibility, efficiency, and quality control are significantly improved. It is a core concept of MLOps (Machine Learning Operations). Pipelines are broadly divided into training pipelines (automating periodic model retraining and A/B testing) and inference pipelines (handling real-time and batch prediction processing). Tools such as Apache Airflow, Kubeflow, MLflow, Amazon SageMaker Pipelines, and Vertex AI Pipelines are commonly used. For LLM applications, building RAG pipelines with LangChain or Dify is the standard approach.