juniorAzure ML
What are Azure ML Pipelines?
Updated May 15, 2026
Short answer
Azure ML Pipelines automate and orchestrate machine learning workflows from data preparation to deployment.
Deep explanation
Azure ML Pipelines provide reusable, modular, and scalable workflows for ML operations. Pipelines consist of independent steps that execute sequentially or in parallel.
Common pipeline stages include:
- Data ingestion
- Data cleaning
- Feature engineering
- Model training
- Evaluation
- Registration
- Deployment
Pipelines improve reproducibility, automation, collaboration, and operational efficiency. They also support scheduling, caching, parameterization, and CI/CD integration.
Real-world example
A logistics company schedules nightly retraining pipelines for shipment delay prediction models.
Common mistakes
- Hardcoding paths, creating monolithic pipelines, not versioning components, and failing to cache reusable outputs.
Follow-up questions
- What is pipeline caching?
- Can pipelines execute in parallel?
- How are pipelines triggered?