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Vertex AI Pipeline

⬢ LIVELLO 2Tecniche
Alto
Impatto sullo stipendio
5 mesi
Tempo di apprendimento
Difficile
Difficoltà
3
Carriere
In sintesi

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.

Cos'è 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.

🔧 STRUMENTI ED ECOSISTEMA
Vertex AI PipelinesKubeflow Pipelines SDKTFXVertex AI MetadataCloud StorageBigQueryCloud BuildContainer Registry

💰 Stipendio per regione

RegioneLivello baseMidLivello esperto
USA$110k$165k$235k
UK£60k£95k£140k
EU€65k€100k€145k
CANADAC$100kC$150kC$215k

🎯 Carriere che usano Vertex AI Pipeline

Analytics Engineer
Data Architect
Data Scientist

❓ Domande frequenti

Vertex AI Pipelines vs. Kubeflow Pipelines?
Vertex AI Pipelines is the managed flavor, same KFP SDK, but Google runs the control plane, metadata store, and execution. You skip cluster ops; you give up some flexibility (custom controllers, on-prem).
When should I use TFX components vs. KFP components?
TFX components ship with strong defaults for typical TFX workflows (ExampleGen, Trainer, Pusher) and write rich metadata. Custom KFP components are right for non-standard steps (custom data sources, third-party APIs, evaluation metrics).
How does lineage tracking work?
Vertex AI Metadata records every artifact (datasets, models, metrics) and execution as a graph. You can query lineage via the SDK or UI to answer 'which training run produced this deployed model?', critical for audits and rollback.
What about cost?
You pay per pipeline run (small overhead) plus the underlying compute (Vertex Training, custom containers). Caching reuses prior step outputs when inputs are unchanged, a big saver on iterative dev.
How do I trigger pipelines from a CI/CD system?
Compile the pipeline to a JSON spec with the KFP SDK, then submit via the Vertex AI client (Python SDK, gcloud, or REST) from Cloud Build / GitHub Actions. Combine with Pub/Sub triggers for event-driven runs.

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