Vertex AI Pipeline
Vertex AI Pipelines is GCP's managed orchestrator for ML workflows built on Kubeflow Pipelines and TFX. ML and platform engineers use it to schedule training, evaluation, and deployment as DAGs of containerized components, versioned, reproducible, and tracked. Mastery takes 4-6 months; senior MLOps roles run $160-260k USA. The skill sits next to Kubeflow, Airflow, MLflow, and SageMaker; it's the GCP-native answer when teams want managed metadata + lineage without running their own KFP cluster.
What is Vertex AI Pipeline
Vertex AI Pipelines is Google Cloud's managed orchestrator for ML workflows. You define a directed acyclic graph (DAG) of containerized components, data ingestion, preprocessing, training, evaluation, deployment, using the Kubeflow Pipelines (KFP) SDK or TFX, then submit it to Vertex AI which runs each step on managed infrastructure with full metadata tracking. It bridges the gap between notebook experiments and production ML systems: every run is versioned, every artifact is lineage-tracked, and outputs cache so iterating on a single step doesn't re-run the whole pipeline.
📋 Before you start
💰 Salary by region
| Region | Junior | Mid | Senior |
|---|---|---|---|
| USA | $110k | $165k | $235k |
| UK | $60k | $95k | $140k |
| EU | $65k | $100k | $145k |
| CANADA | $100k | $150k | $215k |
🎯 Careers using Vertex AI Pipeline
❓ FAQ
Vertex AI Pipelines vs. Kubeflow Pipelines?
When should I use TFX components vs. KFP components?
How does lineage tracking work?
What about cost?
How do I trigger pipelines from a CI/CD system?
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